Journal articles: 'Probability constraints'

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Pandit, Tanmoy, Alaina M. Green, C. Huerta Alderete, Norbert M. Linke, and Raam Uzdin. "Bounds on the recurrence probability in periodically-driven quantum systems." Quantum 6 (April 6, 2022): 682. http://dx.doi.org/10.22331/q-2022-04-06-682.

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Periodically-driven systems are ubiquitous in science and technology. In quantum dynamics, even a small number of periodically-driven spins leads to complicated dynamics. Hence, it is of interest to understand what constraints such dynamics must satisfy. We derive a set of constraints for each number of cycles. For pure initial states, the observable being constrained is the recurrence probability. We use our constraints for detecting undesired coupling to unaccounted environments and drifts in the driving parameters. To illustrate the relevance of these results for modern quantum systems we demonstrate our findings experimentally on a trapped-ion quantum computer, and on various IBM quantum computers. Specifically, we provide two experimental examples where these constraints surpass fundamental bounds associated with known one-cycle constraints. This scheme can potentially be used to detect the effect of the environment in quantum circuits that cannot be classically simulated. Finally, we show that, in practice, testing an n-cycle constraint requires executing only O(n) cycles, which makes the evaluation of constraints associated with hundreds of cycles realistic.

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van de Laar, Thijs, İsmail Şenöz, Ayça Özçelikkale, and Henk Wymeersch. "Chance-Constrained Active Inference." Neural Computation 33, no. 10 (September 16, 2021): 2710–35. http://dx.doi.org/10.1162/neco_a_01427.

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Abstract Active inference (ActInf) is an emerging theory that explains perception and action in biological agents in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. In contrast to prior beliefs, which constrain all realizations of a random variable, we propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation, and demonstrate how such constraints can be used as intrinsic drivers for goal-directed behavior in ActInf. We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing, for example, for a trade-off between robust control and empirical chance constraint violation. Second, we interpret the proposed solution within a message passing framework. Interestingly, the message passing interpretation is not only relevant to the context of ActInf, but also provides a general-purpose approach that can account for chance constraints on graphical models. The chance constraint message updates can then be readily combined with other prederived message update rules without the need for custom derivations. The proposed chance-constrained message passing framework thus accelerates the search for workable models in general and can be used to complement message-passing formulations on generative neural models.

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Flaminiano, John Paul, and Jamil Paolo Francisco. "Firm characteristics and credit constraints among SMEs in the Philippines." Small Business International Review 5, no. 1 (May 31, 2021): e332. http://dx.doi.org/10.26784/sbir.v5i1.332.

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Access to finance is critical to support the growth of small and medium-sized enterprises (SMEs). However, lack of access to adequate financing is one of the biggest obstacles that SMEs face. This paper analyzed the relationship between firm characteristics and credit constraints among SMEs in the Philippines. We determined which firm characteristics are correlated to the predicted probability of being credit-constrained or “quasi-constrained” — i.e., able to borrow from informal sources. Estimates of marginal effects at the means (MEMs) from logistic regressions provide some suggestive evidence that increased firm size, previous purchase of fixed assets, and increased use of digital technologies for accounting and financial management are associated with a lower predicted probability of being credit-constrained. The use of digital technologies in accounting and financial management is also associated with a lower probability of credit constraint in informal financial markets.

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Burgstaller, Bernhard. "Annihilating probability measures under constraints." Quaestiones Mathematicae 29, no. 4 (December 2006): 395–405. http://dx.doi.org/10.2989/16073600609486172.

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Rotea, Mario, and Carlos Lana. "State estimation with probability constraints." International Journal of Control 81, no. 6 (June 2008): 920–30. http://dx.doi.org/10.1080/00207170701531149.

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Gaivoronski, Alexei A., Abdel Lisser, Rafael Lopez, and Hu Xu. "Knapsack problem with probability constraints." Journal of Global Optimization 49, no. 3 (July 1, 2010): 397–413. http://dx.doi.org/10.1007/s10898-010-9566-0.

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Shapira, Assaf. "Kinetically constrained models with random constraints." Annals of Applied Probability 30, no. 2 (April 2020): 987–1006. http://dx.doi.org/10.1214/19-aap1527.

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Kulkarni, Anand J., and K. Tai. "A Probability Collectives Approach with a Feasibility-Based Rule for Constrained Optimization." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/980216.

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This paper demonstrates an attempt to incorporate a simple and generic constraint handling technique to the Probability Collectives (PC) approach for solving constrained optimization problems. The approach of PC optimizes any complex system by decomposing it into smaller subsystems and further treats them in a distributed and decentralized way. These subsystems can be viewed as a Multi-Agent System with rational and self-interested agents optimizing their local goals. However, as there is no inherent constraint handling capability in the PC approach, a real challenge is to take into account constraints and at the same time make the agents work collectively avoiding the tragedy of commons to optimize the global/system objective. At the core of the PC optimization methodology are the concepts of Deterministic Annealing in Statistical Physics, Game Theory and Nash Equilibrium. Moreover, a rule-based procedure is incorporated to handle solutions based on the number of constraints violated and drive the convergence towards feasibility. Two specially developed cases of the Circle Packing Problem with known solutions are solved and the true optimum results are obtained at reasonable computational costs. The proposed algorithm is shown to be sufficiently robust, and strengths and weaknesses of the methodology are also discussed.

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Bhaduri, Saumitra N. "Investment and Capital Market Imperfections: Some Evidence from a Developing Economy, India." Review of Pacific Basin Financial Markets and Policies 11, no. 03 (September 2008): 411–28. http://dx.doi.org/10.1142/s0219091508001416.

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This paper presents a switching regression model of investment decision where the probability of a firm facing financial constraint is endogenously determined. The approach, therefore, obviates the use of a priori criteria to exogenously identify the financially constrained firms, and thereby addresses the potential misclassification problem faced in the existing literature. A sample of 576 Indian manufacturing firms, collected across 15 broad industries is used for this study. The study establishes that financially constrained firms exhibit a much higher investment-cash flow sensitivity than those identified to be unconstrained. It also probes into the possible determinants of financial constraints, and finds empirical support for its hypothesis that young, liquidity constrained and low dividend payout firms are more likely to face financial constraints, when compared to their respective counterparts. This paper also provides some insight into the impact of the ongoing liberalization program on the financial constraints faced by the Indian firms.

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Ayton, Benjamin, Brian Williams, and Richard Camilli. "Measurement Maximizing Adaptive Sampling with Risk Bounding Functions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7511–19. http://dx.doi.org/10.1609/aaai.v33i01.33017511.

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In autonomous exploration a mobile agent must adapt to new measurements to seek high reward, but disturbances cause a probability of collision that must be traded off against expected reward. This paper considers an autonomous agent tasked with maximizing measurements from a Gaussian Process while subject to unbounded disturbances. We seek an adaptive policy in which the maximum allowed probability of failure is constrained as a function of the expected reward. The policy is found using an extension to Monte Carlo Tree Search (MCTS) which bounds probability of failure. We apply MCTS to a sequence of approximating problems, which allows constraint satisfying actions to be found in an anytime manner. Our innovation lies in defining the approximating problems and replanning strategy such that the probability of failure constraint is guaranteed to be satisfied over the true policy. The approach does not need to plan for all measurements explicitly or constrain planning based only on the measurements that were observed. To the best of our knowledge, our approach is the first to enforce probability of failure constraints in adaptive sampling. Through experiments on real bathymetric data and simulated measurements, we show our algorithm allows an agent to take dangerous actions only when the reward justifies the risk. We then verify through Monte Carlo simulations that failure bounds are satisfied.

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Archer, Lan, Parmendra Sharma, and Jen-Je Su. "SME credit constraints and access to informal credit markets in Vietnam." International Journal of Social Economics 47, no. 6 (June 8, 2020): 787–807. http://dx.doi.org/10.1108/ijse-11-2017-0543.

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PurposeA review of literature has documented that accessing formal credit and other banking services has always been a crucial challenge for small and medium-sized enterprises (SMEs). The alternative, therefore, tends to be informal channels. However, the credit constraint vis-à-vis informal channel link does not appear to be well documented in the literature. This study aims to investigate whether credit constraints significantly affect the probability of accessing informal credit, as well as the credit values of Vietnamese SMEs.Design/methodology/approachThis study uses a trinary approach and correlated random-effects Probit and Tobit techniques to avoid the incidental coefficients problem.FindingsThe results suggest that relative to unconstrained and partially constrained firms, fully constrained firms tend to be more active in the informal credit markets, shown by their higher probability of informal credit access and larger credit values.Originality/valueTo the best of authors’ knowledge, this is the first study on Vietnam that takes a different approach to credit constraints and examines their impact on informal credit access. Policy implications arise and are discussed.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2017-0543

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Zhang, Zhi Wei, Bo Wang, and Zhi Gang Zhu. "Adaptive Beam-Forming Based on the Mutative Scale Chaos Optimization." Advanced Materials Research 588-589 (November 2012): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.703.

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In order to improve the SINR of the beam-former in the case of DOA mismatch, the paper raises the probability of constraint beam-forming algorithm based on mutative scale chaos optimization in connection with the worst performance of the best sound beam-forming algorithm. By introducing the probability constraints confidence interval and the signal vector error distribution law in the beam-forming, the constrained optimization problem is eventually turned into a nonlinear optimization problem. Then the global optimal weight is searched by the mutative scale chaos optimization method. Simulations have shown that the algorithm can significantly improve the SINR.

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Yanıkoğlu, İhsan. "Robust reformulations of ambiguous chance constraints with discrete probability distributions." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 9, no. 2 (July 31, 2019): 236–52. http://dx.doi.org/10.11121/ijocta.01.2019.00611.

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This paper proposes robust reformulations of ambiguous chance constraints when the underlying family of distributions is discrete and supported in a so-called ``p-box'' or ``p-ellipsoidal'' uncertainty set. Using the robust optimization paradigm, the deterministic counterparts of the ambiguous chance constraints are reformulated as mixed-integer programming problems which can be tackled by commercial solvers for moderate sized instances. For larger sized instances, we propose a safe approximation algorithm that is computationally efficient and yields high quality solutions. The associated approach and the algorithm can be easily extended to joint chance constraints, nonlinear inequalities, and dependent data without introducing additional mathematical optimization complexity to that of the original robust reformulation. In numerical experiments, we first present our approach over a toy-sized chance constrained knapsack problem. Then, we compare optimality and computational performances of the safe approximation algorithm with those of the exact and the randomized approaches for larger sized instances via Monte Carlo simulation.

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Yue, Michael, and Sara Tehranipoor. "A Novel Probability-Based Logic-Locking Technique: ProbLock." Sensors 21, no. 23 (December 4, 2021): 8126. http://dx.doi.org/10.3390/s21238126.

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Integrated circuit (IC) piracy and overproduction are serious issues that threaten the security and integrity of a system. Logic locking is a type of hardware obfuscation technique where additional key gates are inserted into the circuit. Only the correct key can unlock the functionality of that circuit; otherwise, the system produces the wrong output. In an effort to hinder these threats on ICs, we have developed a probability-based logic-locking technique to protect the design of a circuit. Our proposed technique, called “ProbLock”, can be applied to both combinational and sequential circuits through a critical selection process. We used a filtering process to select the best location of key gates based on various constraints. Each step in the filtering process generates a subset of nodes for each constraint. We also analyzed the correlation between each constraint and adjusted the strength of the constraints before inserting key gates. We tested our algorithm on 40 benchmarks from the ISCAS ’85 and ISCAS ’89 suites. We evaluated ProbLock against a SAT attack and measured how long the attack took to successfully generate a key value. The SAT attack took longer for most benchmarks using ProbLock which proves viable security in hardware obfuscation.

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De Bruyne, Benjamin, Satya N. Majumdar, Henri Orland, and Grégory Schehr. "Generating stochastic trajectories with global dynamical constraints." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (December 1, 2021): 123204. http://dx.doi.org/10.1088/1742-5468/ac3e70.

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Abstract We propose a method to exactly generate Brownian paths x c (t) that are constrained to return to the origin at some future time t f , with a given fixed area A f = ∫ 0 t f d t x c ( t ) under their trajectory. We derive an exact effective Langevin equation with an effective force that accounts for the constraint. In addition, we develop the corresponding approach for discrete-time random walks, with arbitrary jump distributions including Lévy flights, for which we obtain an effective jump distribution that encodes the constraint. Finally, we generalise our method to other types of dynamical constraints such as a fixed occupation time on the positive axis T f = ∫ 0 t f d t Θ x c ( t ) or a fixed generalised quadratic area A f = ∫ 0 t f d t x c 2 ( t ) .

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Porgo, Mohamed, John K. M. Kuwornu, Pam Zahonogo, John Baptist D. Jatoe, and Irene S. Egyir. "Credit constraints and labour allocation decisions in rural Burkina Faso." Agricultural Finance Review 77, no. 2 (July 3, 2017): 257–74. http://dx.doi.org/10.1108/afr-05-2016-0047.

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Purpose Credit is central in labour allocation decisions in smallholder agriculture in developing countries. The purpose of this paper is to analyse the effect of credit constraints on farm households’ labour allocation decisions in rural Burkina Faso. Design/methodology/approach The study used a direct elicitation approach of credit constraints and applied a farm household model to categorize households into four labour market participation regimes. A joint estimation of both the multinomial logit model and probit model was applied on survey data from Burkina Faso to assess the effect of credit constraint on the probability of choosing one of the four alternatives. Findings The results of the probit model showed that households’ endowment of livestock, access to news, and membership to an farmer-based organization were factors lowering the probability of being credit constrained in rural Burkina Faso. The multinomial logit model results showed that credit constraints negatively influenced the likelihood of a farm household to use hired labour in agricultural production and perhaps more importantly it induces farm households to hire out labour off farm. The results also showed that the other components of household characteristics and farm attributes are important factors determining the relative probability of selecting a particular labour market participation regime. Social implications Facilitating access to credit in rural Burkina Faso can encourage farm households to use hired labour in agricultural production and thereby positively impacting farm productivity and relieving unemployment pressures. Originality/value In order to identify the effect of credit constraints on farm households’ labour decisions, this study examined farm households’ decisions of hiring on-farm labour, supplying labour off-farm or simultaneously hiring on-farm labour and supplying family labour off-farm under credit constraints using the direct elicitation approach of credit constraints. To the best of the authors’ knowledge, this study is the first to examine this problem in Burkina Faso.

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Chaudhary, Sapana, and Dileep Kalathil. "Safe Online Convex Optimization with Unknown Linear Safety Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6175–82. http://dx.doi.org/10.1609/aaai.v36i6.20566.

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We study the problem of safe online convex optimization, where the action at each time step must satisfy a set of linear safety constraints. The goal is to select a sequence of actions to minimize the regret without violating the safety constraints at any time step (with high probability). The parameters that specify the linear safety constraints are unknown to the algorithm. The algorithm has access to only the noisy observations of constraints for the chosen actions. We propose an algorithm, called the Safe Online Projected Gradient Descent (SO-PGD) algorithm, to address this problem. We show that, under the assumption of availability of a safe baseline action, the SO-PGD algorithm achieves a regret O(T^{2/3}). While there are many algorithms for online convex optimization (OCO) problems with safety constraints available in the literature, they allow constraint violations during learning/optimization, and the focus has been on characterizing the cumulative constraint violations. To the best of our knowledge, ours is the first work that provides an algorithm with provable guarantees on the regret, without violating the linear safety constraints (with high probability) at any time step.

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Huang, Y. Q., S. L. Nie, and H. Ji. "Identification of Contamination Control Strategy for Fluid Power System Using an Inexact Chance-Constrained Integer Program." Journal of Applied Mathematics 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/146413.

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An inexact chance-constrained integer programming (ICIP) method is developed for planning contamination control of fluid power system (FPS). The ICIP is derived by incorporating chance-constrained programming (CCP) within an interval mixed integer linear programming (IMILP) framework, such that uncertainties presented in terms of probability distributions and discrete intervals can be handled. It can also help examine the reliability of satisfying (or risk of violating) system constraints under uncertainty. The developed method is applied to a case of contamination control planning for one typical FPS. Interval solutions associated with risk levels of constraint violation are obtained. They can be used for generating decision alternatives and thus help designers identify desired strategies under various environmental, economic, and system reliability constraints. Generally, willingness to take a higher risk of constraint violation will guarantee a lower system cost; a strong desire to acquire a lower risk will run into a higher system cost. Thus, the method provides not only decision variable solutions presented as stable intervals but also the associated risk levels in violating the system constraints. It can therefore support an in-depth analysis of the tradeoff between system cost and system-failure risk.

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Kostic, Aleksandar, and Milena Bozic. "Constraints on probability distributions of grammatical forms." Psihologija 40, no. 1 (2007): 5–35. http://dx.doi.org/10.2298/psi0701005k.

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In this study we investigate the constraints on probability distribution of grammatical forms within morphological paradigms of Serbian language, where paradigm is specified as a coherent set of elements with defined criteria for inclusion. Thus, for example, in Serbian all feminine nouns that end with the suffix "a" in their nominative singular form belong to the third declension, the declension being a paradigm. The notion of a paradigm could be extended to other criteria as well, hence, we can think of noun cases, irrespective of grammatical number and gender, or noun gender, irrespective of case and grammatical number, also as paradigms. We took the relative entropy as a measure of homogeneity of probability distribution within paradigms. The analysis was performed on 116 morphological paradigms of typical Serbian and for each paradigm the relative entropy has been calculated. The obtained results indicate that for most paradigms the relative entropy values fall within a range of 0.75 - 0.9. Nonhomogeneous distribution of relative entropy values allows for estimating the relative entropy of the morphological system as a whole. This value is 0.69 and can tentatively be taken as an index of stability of the morphological system.

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Andrieu, Laetitia, Guy Cohen, and Felisa J. Vázquez-Abad. "Gradient-based simulation optimization under probability constraints." European Journal of Operational Research 212, no. 2 (July 2011): 345–51. http://dx.doi.org/10.1016/j.ejor.2011.01.049.

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Neuhaus, Walther. "Optimal Estimation Under Linear Constraints." ASTIN Bulletin 26, no. 2 (November 1996): 233–45. http://dx.doi.org/10.2143/ast.26.2.563222.

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AbstractThis paper shows how a multivariate Bayes estimator can be adjusted to satisfy a set of linear constraints. In the direct approach, the constraint is enforced by a restriction on the class of admissible estimators. In an alternative approach, the constraint is merely encouraged by a mixed risk function which penalises misbalance between the estimator and the constraint. The adjustment to the optimal unconstrained estimator is shown to depend on the risk function and the linear constraints only, not on the probability model underlying the Bayes estimator. Two practical examples are given, one of which involves reconciliation of independently assessed share values with current market values.

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Kharroubi, Idris, Thomas Lim, and Xavier Warin. "Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process." Monte Carlo Methods and Applications 27, no. 1 (January 15, 2021): 27–55. http://dx.doi.org/10.1515/mcma-2020-2080.

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Abstract We study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

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GOERDT, ANDREAS. "On Random Betweenness Constraints." Combinatorics, Probability and Computing 19, no. 5-6 (October 5, 2010): 775–90. http://dx.doi.org/10.1017/s0963548310000313.

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Ordering constraints are formally analogous to instances of the satisfiability problem in conjunctive normal form, but instead of a boolean assignment we consider a linear ordering of the variables in question. A clause becomes true given a linear ordering if and only if the relative ordering of its variables obeys the constraint considered.The naturally arising satisfiability problems are NP-complete for many types of constraints. We look at random ordering constraints. Previous work of the author shows that there is a sharp unsatisfiability threshold for certain types of constraints. The value of the threshold, however, is essentially undetermined. We pursue the problem of approximating the precise value of the threshold. We show that random instances of the betweenness constraint are satisfiable with high probability if the number of randomly picked clauses is ≤0.92n, where n is the number of variables considered. This improves the previous bound, which is <0.82n random clauses. The proof is based on a binary relaxation of the betweenness constraint and involves some ideas not used before in the area of random ordering constraints.

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Chein, Flávia, and Cristine Pinto. "Wealth, Credit Constraints and Small Firms’ Investment: Evidence from Brazil." Brazilian Review of Econometrics 40, no. 1 (August 17, 2020): 59. http://dx.doi.org/10.12660/bre.v40n12020.80462.

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<span>We explore the effect of wealth on occupational choice to find out whether small firms in Brazil face credit constraint and how occupational choice relates to investments. Using an instrumental variable approach, we find evidence that occupational choice is, in fact, constrained by wealth and this constraint is related to investment decisions. Our main results show that the probability of investment increases by 0.45 when the individual is an employer, controlling by entrepreneurs characteristics and sector, suggesting that the constraints at the credit market may affect investment decisions of small firms in Brazil. The effect of being an employer on credit access is about 0.320, which supports the idea that employers have more access to credit than the self-employed.</span>

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Feldman, Michal, and Ofir Geri. "Do Capacity Constraints Constrain Coalitions?" ACM Transactions on Economics and Computation 5, no. 1 (November 3, 2016): 1–26. http://dx.doi.org/10.1145/2955090.

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Bopardikar, Shaunak D., Brendan Englot, and Alberto Speranzon. "Multiobjective Path Planning: Localization Constraints and Collision Probability." IEEE Transactions on Robotics 31, no. 3 (June 2015): 562–77. http://dx.doi.org/10.1109/tro.2015.2411371.

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Leblanc, Hugues, and Peter Roeper. "Getting the Constraints on Popper's Probability Functions Right." Philosophy of Science 60, no. 1 (March 1993): 151–57. http://dx.doi.org/10.1086/289723.

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van Ackooij, Wim, and Jérôme Malick. "Eventual convexity of probability constraints with elliptical distributions." Mathematical Programming 175, no. 1-2 (January 19, 2018): 1–27. http://dx.doi.org/10.1007/s10107-018-1230-3.

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Li, Yi, Jun Peng, Fu Jiang, Kaiyang Liu, and Xiaoyong Zhang. "Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (March 20, 2015): 197–204. http://dx.doi.org/10.20965/jaciii.2015.p0197.

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To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

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Galán, Severino F., and Ole J. Mengshoel. "Constraint Handling Using Tournament Selection: Abductive Inference in Partly Deterministic Bayesian Networks." Evolutionary Computation 17, no. 1 (March 2009): 55–88. http://dx.doi.org/10.1162/evco.2009.17.1.55.

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Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.

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Zhu, Hui, Xinkai Kuang, Tao Su, Ziyu Chen, Biao Yu, and Bichun Li. "Dual-Constraint Registration LiDAR SLAM Based on Grid Maps Enhancement in Off-Road Environment." Remote Sensing 14, no. 22 (November 11, 2022): 5705. http://dx.doi.org/10.3390/rs14225705.

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Aiming at the influence of fewer feature points and dynamic obstacles on location and mapping in off-road environments, we propose a dual-constraint LiDAR-based Simultaneous Localization and Mapping (SLAM) scheme. By abstracting LiDAR registration into two constraints, namely, in-window constraints and out-of-window constraints, the in-window constraints allow our scheme to compromise between accuracy and real-time performance, and out-of-window constraints can exploit optimized variables to provide richer constraint information. The advantages of incremental SLAM map construction can be used to design a variety of map forms. Although the variables outside the window are no longer involved in the optimization, we can use the two-dimensional probability grid map to provide binary semantic information and dynamic weights for the constraints outside the window to enhance the registration accuracy. Finally, we conducted experiments in off-road environment and compared with the mainstream LiDAR SLAM scheme, which can prove that our scheme has more advantages in accuracy.

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Chen, Juan, Dongdong Shi, Jian Ma, and Jun Chen. "Effect of height constraints on unidirectional pedestrian flow." Journal of Statistical Mechanics: Theory and Experiment 2022, no. 8 (August 1, 2022): 083403. http://dx.doi.org/10.1088/1742-5468/ac7e4a.

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Abstract In case of fire evacuation, pedestrians would have to utmost minimize their exposure to smoke and meanwhile escape as fast as possible. Under these circumstances, the smoke layer forms a height constraint for the pedestrians. Understanding pedestrian movement features, especially in response to the discrepancy smoke layer heights, is vital to the evacuation safety evaluation. In the present paper, a series of unidirectional pedestrian movement experiments under controlled laboratory conditions were performed by setting five different height constraints (H = 1.0, 1.2, 1.4, 1.6 and 2.0 m). The trajectories of each pedestrian in all scenarios have been extracted from video recordings of the experiments. With these data, typical pedestrian behaviors including lane formation and overtaking have been identified and discussed. Time-space characteristics were also detailed to explore the effect of height constraint. As indicated by the distribution of the nth-nearest neighbor, the available heights only have a slight influence on the collision avoidance feature. However, the height constraints do affect the speed and specific flow with increasing pedestrian density. For a height constraint of H = 1.2 m, the pedestrian speed presented a clear drop when compared with the height constraint of H = 1.4 m. What is more, in the density range of this study, the maximum flow is not observed for height constraints of H = 1.4, 1.6 and 2.0 m, however, the flow reaches its maximum value of 0.58 and 0.78 for constraint heights H = 1.0 and 1.2 m, respectively. These findings provide not only fundamental data of pedestrian movement with height constraints, but also new insights into the complex pedestrian movement features in an emergency.

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Boonen, Tim J., and Mario Ghossoub. "BILATERAL RISK SHARING WITH HETEROGENEOUS BELIEFS AND EXPOSURE CONSTRAINTS." ASTIN Bulletin 50, no. 1 (January 2020): 293–323. http://dx.doi.org/10.1017/asb.2019.39.

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AbstractThis paper studies bilateral risk sharing under no aggregate uncertainty, where one agent has Expected-Utility preferences and the other agent has Rank-dependent utility preferences with a general probability distortion function. We impose exogenous constraints on the risk exposure for both agents, and we allow for any type or level of belief heterogeneity. We show that Pareto-optimal risk-sharing contracts can be obtained via a constrained utility maximization under a participation constraint of the other agent. This allows us to give an explicit characterization of optimal risk-sharing contracts. In particular, we show that an optimal risk-sharing contract contains allocations that are monotone functions of the likelihood ratio, where the latter is obtained from Lebesgue’s Decomposition Theorem.

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Hamada, Michiaki. "Direct Updating of an RNA Base-Pairing Probability Matrix with Marginal Probability Constraints." Journal of Computational Biology 19, no. 12 (December 2012): 1265–76. http://dx.doi.org/10.1089/cmb.2012.0215.

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Hsieh, Tzung-Yuan, Shaung-Shii Chuang, and Ching-Chung Lin. "Impact of Tick-Size Reduction on the Market Liquidity — Evidence from the Emerging Order-Driven Market." Review of Pacific Basin Financial Markets and Policies 11, no. 04 (December 2008): 591–616. http://dx.doi.org/10.1142/s0219091508001490.

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Empirical studies on the influence of tick-size reduction towards market liquidity have focused almost exclusively on quote-driven markets in developed nations, and generally their findings are based on time periods of less than one year. This work investigates the influence of tick-size reduction and the relaxations of binding-constraint probability on market liquidity in the Taiwanese stock market, an emerging order-driven market, starting on March 1, 2005. The empirical results show that the spread, depth, market liquidity, and binding-constraint probability all decrease following the tick-size reduction, especially for low-priced stocks. These results can be attributed to relaxation of binding constraints. Additionally, stocks that are frequently traded, have larger market capitalization, or have restrictive binding constraints, experience considerable declines in spread, depth, and market liquidity following tick-size reduction. Trading activity plays an important role in explaining changes in spread, depth, market liquidity, and binding constraints. Thus, tick-size reduction in the Taiwanese Stock Market can increase market efficiency and reduce the investors' trading costs.

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Fusco, Giovanni, and Monica Motta. "No Infimum Gap and Normality in Optimal Impulsive Control Under State Constraints." Set-Valued and Variational Analysis 29, no. 2 (April 27, 2021): 519–50. http://dx.doi.org/10.1007/s11228-021-00576-2.

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AbstractIn this paper we consider an impulsive extension of an optimal control problem with unbounded controls, subject to endpoint and state constraints. We show that the existence of an extended-sense minimizer that is a normal extremal for a constrained Maximum Principle ensures that there is no gap between the infima of the original problem and of its extension. Furthermore, we translate such relation into verifiable sufficient conditions for normality in the form of constraint and endpoint qualifications. Links between existence of an infimum gap and normality in impulsive control have previously been explored for problems without state constraints. This paper establishes such links in the presence of state constraints and of an additional ordinary control, for locally Lipschitz continuous data.

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Sanderson, Benjamin M., Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti. "The potential for structural errors in emergent constraints." Earth System Dynamics 12, no. 3 (August 23, 2021): 899–918. http://dx.doi.org/10.5194/esd-12-899-2021.

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Abstract. Studies of emergent constraints have frequently proposed that a single metric can constrain future responses of the Earth system to anthropogenic emissions. Here, we illustrate that strong relationships between observables and future climate across an ensemble can arise from common structural model assumptions with few degrees of freedom. Such cases have the potential to produce strong yet overconfident constraints when processes are represented in a common, oversimplified fashion throughout the ensemble. We consider these issues in the context of a collection of published constraints and argue that although emergent constraints are potentially powerful tools for understanding ensemble response variation and relevant observables, their naïve application to reduce uncertainties in unknown climate responses could lead to bias and overconfidence in constrained projections. The prevalence of this thinking has led to literature in which statements are made on the probability bounds of key climate variables that were confident yet inconsistent between studies. Together with statistical robustness and a mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that can arise from shared, oversimplified modelling assumptions that impact both present and future climate simulations in order to mitigate against the influence of shared structural biases.

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38

Binh, Le Huu, Vo Thanh Tu, and Nguyen Van Tam. "SLBQT-DSR: SOURCE-BASED LOAD BALANCING ROUTING ALGORITHM UNDER CONSTRAINTS OF QUALITY OF TRANSMISION FOR MANET." Journal of Computer Science and Cybernetics 34, no. 3 (November 23, 2018): 265–82. http://dx.doi.org/10.15625/1813-9663/34/3/13083.

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The routing technique under the constraints of the quality of transmision (QoT) in mobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoT of the data transmission routes is improved. However, for the network models with mesh topologies such as MANET, The routing technique under the constraints of QoT can increase the bottlenecks due to the unbalanced traffic load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that, the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, bit error rate, blocking probability of the data packets and throughput.The routing technique under the constraints of the quality of transmision (QoT) inmobile ad hoc networks (MANET) has been studied widely recently. For this routing technique, QoTof the data transmission routes is improved. However, for the network models with mesh topologiessuch as MANET, The routing technique under the constraints of QoT can increase the bottlenecksdue to the unbalanced trac load. In this paper, we improve the DSR protocol by using the source-based load balancing in combined with the QoT constraint. The simulation results have shown that,the proposed algorithm outperforms the original algorithms in terms of the signal to noise ratio, biterror rate, blocking probability of the data packets and throughput.

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Ankirchner, Stefan, and Maike Klein. "Bayesian sequential testing with expectation constraints." ESAIM: Control, Optimisation and Calculus of Variations 26 (2020): 51. http://dx.doi.org/10.1051/cocv/2019045.

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We study a stopping problem arising from a sequential testing of two simple hypotheses H0 and H1 on the drift rate of a Brownian motion. We impose an expectation constraint on the stopping rules allowed and show that an optimal stopping rule satisfying the constraint can be found among the rules of the following type: stop if the posterior probability for H1 attains a given lower or upper barrier; or stop if the posterior probability comes back to a fixed intermediate point after a sufficiently large excursion. Stopping at the intermediate point means that the testing is abandoned without accepting H0 or H1. In contrast to the unconstrained case, optimal stopping rules, in general, cannot be found among interval exit times. Thus, optimal stopping rules in the constrained case qualitatively differ from optimal rules in the unconstrained case.

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Patton, Kenneth, Jonathan Blazek, Klaus Honscheid, Eric Huff, Peter Melchior, Ashley J. Ross, and Eric Suchyta. "Cosmological constraints from the convergence 1-point probability distribution." Monthly Notices of the Royal Astronomical Society 472, no. 1 (June 29, 2017): 439–46. http://dx.doi.org/10.1093/mnras/stx1626.

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Lee, Jemin, Hano Wang, Jeffrey G. Andrews, and Daesik Hong. "Outage Probability of Cognitive Relay Networks with Interference Constraints." IEEE Transactions on Wireless Communications 10, no. 2 (February 2011): 390–95. http://dx.doi.org/10.1109/twc.2010.120310.090852.

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Chun, Junho, Junho Song, and Glaucio H. Paulino. "Structural topology optimization under constraints on instantaneous failure probability." Structural and Multidisciplinary Optimization 53, no. 4 (November 20, 2015): 773–99. http://dx.doi.org/10.1007/s00158-015-1296-y.

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Bayraktar, Erhan, and Virginia R. Young. "Minimizing the probability of lifetime ruin under borrowing constraints." Insurance: Mathematics and Economics 41, no. 1 (July 2007): 196–221. http://dx.doi.org/10.1016/j.insmatheco.2006.10.015.

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Won, Bo-Yeong, and Andrew B. Leber. "Spatial constraints on probability learning in visual working memory." Visual Cognition 25, no. 1-3 (March 16, 2017): 34–50. http://dx.doi.org/10.1080/13506285.2017.1346738.

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Asimit, Alexandru V., Alexandru M. Badescu, Tak Kuen Siu, and Yuriy Zinchenko. "Capital requirements and optimal investment with solvency probability constraints." IMA Journal of Management Mathematics 26, no. 4 (January 22, 2014): 345–75. http://dx.doi.org/10.1093/imaman/dpt029.

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46

Rahmanov, Ramiz. "Financial Constraints and Export Participation of SMEs in Post-Communist Countries." Acta Oeconomica 69, no. 2 (June 2019): 289–319. http://dx.doi.org/10.1556/032.2019.69.2.7.

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This paper examines the impact of medium- and short-term financial constraints on the probability of export participation of SMEs in 28 post-communist countries. The regression analysis conducted over the cross-sectional sample of SMEs taken from the BEEPS III-IV-V shows that the medium- and short-term financial constraints produce a significantly negative effect on the probability of exporting. Although there exist arguments for why the effects of medium- and short-term financial constraints can differ from each other, both the medium- and short-term financial constraints appear to reduce the probability of exporting equally by 25%. The regression results also suggest that more productive, innovative, and larger SMEs, and also SMEs with international quality certificates are more likely to export. When the regressions are separately estimated for the first-time and continuous exporters, it appears that only the probability of exporting of continuous exporters is significantly sensitive to the financial constraints. Furthermore, the regressions separately run for the direct and indirect exporters show that the financial constraints have a larger effect on the probability of exporting of indirect exporters. The heterogeneity analysis shows that there is a significant heterogeneity in the effects of medium- and short-term financial constraints on the likelihood of exporting across regions, industries, periods, and firm types.

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PENG, YUN, ZHONGLI DING, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REVISION WITH PROBABILISTIC CONSTRAINTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 03 (May 17, 2012): 317–37. http://dx.doi.org/10.1142/s021848851250016x.

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This paper deals with an important probabilistic knowledge integration problem: revising a Bayesian network (BN) to satisfy a set of probability constraints representing new or more specific knowledge. We propose to solve this problem by adopting IPFP (iterative proportional fitting procedure) to BN. The resulting algorithm E-IPFP integrates the constraints by only changing the conditional probability tables (CPT) of the given BN while preserving the network structure; and the probability distribution of the revised BN is as close as possible to that of the original BN. Two variations of E-IPFP are also proposed: 1) E-IPFP-SMOOTH which deals with the situation where the probabilistic constraints are inconsistent with each other or with the network structure of the given BN; and 2) D-IPFP which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems.

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48

Fang, Zhide. "Extrapolation designs with constraints." Canadian Journal of Statistics 31, no. 4 (December 2003): 457–68. http://dx.doi.org/10.2307/3315856.

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Ma, Yanbo, Kaiyue Liu, Zheng Li, and Xiang Chen. "Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration." International Journal of Environmental Research and Public Health 19, no. 20 (October 21, 2022): 13685. http://dx.doi.org/10.3390/ijerph192013685.

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This paper proposes an operating room (OR) scheduling model to assign a group of next-day patients to ORs while adhering to OR availability, priorities, and OR overtime constraints. Existing studies usually consider OR scheduling problems by ignoring the influence of uncertainties in surgery durations on the OR assignment. In this paper, we address this issue by formulating accurate patient waiting times as the cumulative sum of uncertain surgery durations from the robust discrete approach point of view. Specifically, by considering the patients’ uncertain surgery duration, we formulate the robust OR scheduling model to minimize the sum of the fixed OR opening cost, the patient waiting penalty cost, and the OR overtime cost. Then, we adopt the box uncertainty set to specify the uncertain surgery duration, and a robustness coefficient is introduced to control the robustness of the model. This resulting robust model is essentially intractable in its original form because there are uncertain variables in both the objective function and constraint. To make this model solvable, we then transform it into a Mixed Integer Linear Programming (MILP) model by employing the robust discrete optimization theory and the strong dual theory. Moreover, to evaluate the reliability of the robust OR scheduling model under different robustness coefficients, we theoretically analyze the constraint violation probability associated with overtime constraints. Finally, an in-depth numerical analysis is conducted to verify the proposed model’s effectiveness and to evaluate the robustness coefficient’s impact on the model performance. Our analytical results indicate the following: (1) With the robustness coefficient, we obtain the tradeoff relationship between the total management cost and the constraint violation probability, i.e., a smaller robustness coefficient yields remarkably lower total management cost at the expense of a noticeably higher constraint violation probability and vice versa. (2) The obtained total management cost is sensitive to small robustness coefficient values, but it hardly changes as the robustness coefficient increases to a specific value. (3) The obtained total management cost becomes increasingly sensitive to the perturbation factor with the decrease in constraint violation probability.

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Gaydos, Travis L., Nancy E. Heckman, Mark Kirkpatrick, J. R. Stinchcombe, Johanna Schmitt, Joel Kingsolver, and J. S. Marron. "Visualizing genetic constraints." Annals of Applied Statistics 7, no. 2 (June 2013): 860–82. http://dx.doi.org/10.1214/12-aoas603.

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Journal articles on the topic 'Probability constraints'

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