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Artykuły w czasopismach na temat „Stochastic sampling algorithms”

Autor: Grafiati

Data publikacji: 5 czerwca 2025

Data aktualizacji: 2 sierpnia 2025

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1

Mohamed, Linah, Mike Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." SPE Journal 15, no. 01 (2009): 31–38. http://dx.doi.org/10.2118/119139-pa.

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Summary History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir (e.g., from analog outcrop data) and update our reservoir models with observations (e.g., from production data or time-lapse seismic). The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history-matched models is the sampling algorithm us

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2

Devir, Zvi, and Michael Lindenbaum. "Adaptive Range Sampling Using a Stochastic Model." Journal of Computing and Information Science in Engineering 7, no. 1 (2006): 20–25. http://dx.doi.org/10.1115/1.2432899.

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We consider the task of sequential point sampling for three-dimensional structure reconstruction and focus on terrestrial topographic mapping using a laser range scanner. Both the sampling and the reconstruction rely on a stochastic model of the sampled object. We describe several algorithms for sequential point sampling including a new adaptive algorithm that is specifically designed for mechanical devices and produces grid-like sampling patterns. Experimental results verify that relying on the stochastic model indeed leads to efficient sampling associated with accurate surface reconstruction

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3

Chowdhury and, Anirban Narayan, and Rolando D. Somma. "Quantum algorithms for Gibbs sampling and hitting-time estimation." Quantum Information and Computation 17, no. 1&2 (2017): 41–64. http://dx.doi.org/10.26421/qic17.1-2-3.

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We present quantum algorithms for solving two problems regarding stochastic processes. The first algorithm prepares the thermal Gibbs state of a quantum system and runs in time almost linear in p Nβ/Z and polynomial in log(1/epsilon), where N is the Hilbert space dimension, β is the inverse temperature, Z is the partition function, and epsilon is the desired precision of the output state. Our quantum algorithm exponentially improves the complexity dependence on 1/epsilon and polynomially improves the dependence on β of known quantum algorithms for this problem. The second algorithm estimates t

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4

Prellberg, Thomas. "Rare event sampling with stochastic growth algorithms." EPJ Web of Conferences 44 (2013): 01001. http://dx.doi.org/10.1051/epjconf/20134401001.

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5

Mooasvi, Azam, and Adrian Sandu. "APPROXIMATE EXPONENTIAL ALGORITHMS TO SOLVE THE CHEMICAL MASTER EQUATION." Mathematical Modelling and Analysis 20, no. 3 (2015): 382–95. http://dx.doi.org/10.3846/13926292.2015.1048760.

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This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution. These algorithms make use of various approximations of the matrix exponential to evolve probability densities in time. A sampling of the approximate solutions of the chemical master equation is used to derive accelerated stochastic simulation algorithms. Numerical experiments compare the new methods with the established stochastic simulation algorithm and the tau-leaping method.

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6

Swamy, Chaitanya, and David B. Shmoys. "Sampling-Based Approximation Algorithms for Multistage Stochastic Optimization." SIAM Journal on Computing 41, no. 4 (2012): 975–1004. http://dx.doi.org/10.1137/100789269.

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7

Stoltz, Gabriel. "Path sampling with stochastic dynamics: Some new algorithms." Journal of Computational Physics 225, no. 1 (2007): 491–508. http://dx.doi.org/10.1016/j.jcp.2006.12.006.

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8

Vatter, Thibault. "Simulating Copulas: Stochastic Models, Sampling Algorithms, and Applications." Journal of the American Statistical Association 115, no. 529 (2020): 481–82. http://dx.doi.org/10.1080/01621459.2020.1721244.

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9

Dempster, M. A. H. "Sequential Importance Sampling Algorithms for Dynamic Stochastic Programming." Journal of Mathematical Sciences 133, no. 4 (2006): 1422–44. http://dx.doi.org/10.1007/s10958-006-0058-1.

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10

Rezvanian, Alireza, and Mohammad Reza Meybodi. "Sampling algorithms for stochastic graphs: A learning automata approach." Knowledge-Based Systems 127 (July 2017): 126–44. http://dx.doi.org/10.1016/j.knosys.2017.04.012.

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11

Yiou, Pascal, and Aglaé Jézéquel. "Simulation of extreme heat waves with empirical importance sampling." Geoscientific Model Development 13, no. 2 (2020): 763–81. http://dx.doi.org/10.5194/gmd-13-763-2020.

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Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modif

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12

Qian, Guoqi, Calyampudi Radhakrishna Rao, Xiaoying Sun, and Yuehua Wu. "Boosting association rule mining in large datasets via Gibbs sampling." Proceedings of the National Academy of Sciences 113, no. 18 (2016): 4958–63. http://dx.doi.org/10.1073/pnas.1604553113.

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Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling–induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as

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13

Carius, Jan, René Ranftl, Farbod Farshidian, and Marco Hutter. "Constrained stochastic optimal control with learned importance sampling: A path integral approach." International Journal of Robotics Research 41, no. 2 (2021): 189–209. http://dx.doi.org/10.1177/02783649211047890.

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Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments t

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14

Staudacher, Jochen, and Tim Pollmann. "Assessing Antithetic Sampling for Approximating Shapley, Banzhaf, and Owen Values." AppliedMath 3, no. 4 (2023): 957–88. http://dx.doi.org/10.3390/appliedmath3040049.

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Computing Shapley values for large cooperative games is an NP-hard problem. For practical applications, stochastic approximation via permutation sampling is widely used. In the context of machine learning applications of the Shapley value, the concept of antithetic sampling has become popular. The idea is to employ the reverse permutation of a sample in order to reduce variance and accelerate convergence of the algorithm. We study this approach for the Shapley and Banzhaf values, as well as for the Owen value which is a solution concept for games with precoalitions. We combine antithetic sampl

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15

Fill, James Allen. "The Move-to-Front Rule: A Case Study for two Perfect Sampling Algorithms." Probability in the Engineering and Informational Sciences 12, no. 3 (1998): 283–302. http://dx.doi.org/10.1017/s0269964800005192.

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The elementary problem of exhaustively sampling a finite population without replacement is used as a nonreversible test case for comparing two recently proposed MCMC algorithms for perfect sampling, one based on backward coupling and the other on strong stationary duality. The backward coupling algorithm runs faster in this case, but the duality-based algorithm is unbiased for user impatience. An interesting by-product of the analysis is a new and simple stochastic interpretation of a mixing-time result for the move-to-front rule.

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16

Baker, James E. "Providing Accurate yet Maximally Consistent Stochastic Sampling for Genetic Algorithms." Intelligent Automation & Soft Computing 5, no. 1 (1999): 43–56. http://dx.doi.org/10.1080/10798587.1999.10750750.

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17

Gupta, Anupam, Martin Pál, R. Ravi, and Amitabh Sinha. "Sampling and Cost-Sharing: Approximation Algorithms for Stochastic Optimization Problems." SIAM Journal on Computing 40, no. 5 (2011): 1361–401. http://dx.doi.org/10.1137/080732250.

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18

Shao, Litian. "Utilizing Multi-Armed Bandit Algorithms for Advertising: An In-Depth Case Study on an Online Retail Platform's Advertising Campaign." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 217–23. http://dx.doi.org/10.54097/1gv35d75.

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In recent years, the advertising sector has increasingly embraced Multi-armed Bandit Algorithms (MAB) for their versatile applications. This article delves into four stochastic MAB algorithms: the Explore-Then-Commit (ETC), the Upper Confidence Bound (UCB), the Thompson-Sampling (TS), and their respective variants, applied in an online shopping platform's advertising campaign to optimize click-through rates. Our findings indicate that each of these algorithms successfully identifies the most effective advertisement. Both the UCB and TS algorithms demonstrate logarithmic regret relative to the

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19

Fouad, Ahmed M., Mohamed Saleh, and Amir F. Atiya. "A Novel Quota Sampling Algorithm for Generating Representative Random Samples given Small Sample Size." International Journal of System Dynamics Applications 2, no. 1 (2013): 97–113. http://dx.doi.org/10.4018/ijsda.2013010105.

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In this paper, a novel algorithm is proposed for sampling from discrete probability distributions using the probability proportional to size sampling method, which is a special case of Quota sampling method. The motivation for this study is to devise an efficient sampling algorithm that can be used in stochastic optimization problems -- when there is a need to minimize the sample size. Several experiments have been conducted to compare the proposed algorithm with two widely used sample generation methods, the Monte Carlo using inverse transform, and quasi-Monte Carlo algorithms. The proposed a

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20

Kiss, Oriel, Michele Grossi, and Alessandro Roggero. "Importance sampling for stochastic quantum simulations." Quantum 7 (April 13, 2023): 977. http://dx.doi.org/10.22331/q-2023-04-13-977.

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Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in the Hamiltonian, and can therefore be challenging to implement on near-term, as well as early fault-tolerant quantum devices. An efficient solution is given by the stochastic compilation protocol known as qDrift, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary

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21

AKBARI TORKESTANI, JAVAD, and MOHAMMAD REZA MEYBODI. "LEARNING AUTOMATA-BASED ALGORITHMS FOR FINDING MINIMUM WEAKLY CONNECTED DOMINATING SET IN STOCHASTIC GRAPHS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 06 (2010): 721–58. http://dx.doi.org/10.1142/s0218488510006775.

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A weakly connected dominating set (WCDS) of graph G is a subset of G so that the vertex set of the given subset and all vertices with at least one endpoint in the subset induce a connected sub-graph of G. The minimum WCDS (MWCDS) problem is known to be NP-hard, and several approximation algorithms have been proposed for solving MWCDS in deterministic graphs. However, to the best of our knowledge no work has been done on finding the WCDS in stochastic graphs. In this paper, a definition of the MWCDS problem in a stochastic graph is first presented and then several learning automata-based algori

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22

Cheng, J., and M. J. Druzdzel. "AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks." Journal of Artificial Intelligence Research 13 (October 1, 2000): 155–88. http://dx.doi.org/10.1613/jair.764.

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Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical proper

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23

Hedar, Abdel-Rahman, Amira A. Allam, and Alaa Fahim. "Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems." Applied Sciences 10, no. 19 (2020): 6937. http://dx.doi.org/10.3390/app10196937.

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Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One common approach to dealing with these problems is to combine sampling methods with optimal search methods. Sampling techniques have a serious problem when the sample size is small, so estimating the objective function values with noise is not accurate in this case. In this research, a new sampling technique is proposed based on fuzzy logic to

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24

Poli, Riccardo. "Dynamics and Stability of the Sampling Distribution of Particle Swarm Optimisers via Moment Analysis." Journal of Artificial Evolution and Applications 2008 (March 30, 2008): 1–10. http://dx.doi.org/10.1155/2008/761459.

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For stochastic optimisation algorithms, knowing the probability distribution with which an algorithm allocates new samples in the search space is very important, since this explains how the algorithm really works and is a prerequisite to being able to match algorithms to problems. This is the only way to beat the limitations highlighted by the no-free lunch theory. Yet, the sampling distribution for velocity-based particle swarm optimisers has remained a mystery for the whole of the first decade of PSO research. In this paper, a method is presented that allows one to exactly determine all the

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25

Franzese, Giulio, Dimitrios Milios, Maurizio Filippone, and Pietro Michiardi. "A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization." Entropy 23, no. 11 (2021): 1426. http://dx.doi.org/10.3390/e23111426.

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Stochastic gradient sg-based algorithms for Markov chain Monte Carlo sampling (sgmcmc) tackle large-scale Bayesian modeling problems by operating on mini-batches and injecting noise on sgsteps. The sampling properties of these algorithms are determined by user choices, such as the covariance of the injected noise and the learning rate, and by problem-specific factors, such as assumptions on the loss landscape and the covariance of sg noise. However, current sgmcmc algorithms applied to popular complex models such as Deep Nets cannot simultaneously satisfy the assumptions on loss landscapes and

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26

Ou, Mingdong, Nan Li, Cheng Yang, Shenghuo Zhu, and Rong Jin. "Semi-Parametric Sampling for Stochastic Bandits with Many Arms." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7933–40. http://dx.doi.org/10.1609/aaai.v33i01.33017933.

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We consider the stochastic bandit problem with a large candidate arm set. In this setting, classic multi-armed bandit algorithms, which assume independence among arms and adopt non-parametric reward model, are inefficient, due to the large number of arms. By exploiting arm correlations based on a parametric reward model with arm features, contextual bandit algorithms are more efficient, but they can also suffer from large regret in practical applications, due to the reward estimation bias from mis-specified model assumption or incomplete features. In this paper, we propose a novel Bayesian fra

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27

Balu, Radhakrishnan, Dale Shires, and Raju Namburu. "A quantum algorithm for uniform sampling of models of propositional logic based on quantum probability." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 16, no. 1 (2016): 57–65. http://dx.doi.org/10.1177/1548512916648232.

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We describe a class of quantum algorithms to generate models of propositional logic with equal probability. We consider quantum stochastic flows that are the quantum analogues of classical Markov chains and establish a relation between fixed points on the two flows. We construct chains inspired by von Neumann algorithms using uniform measures as fixed points to construct the corresponding irreversible quantum stochastic flows. We formulate sampling models of propositions in the framework of adiabatic quantum computing and solve the underlying satisfiability instances. Satisfiability formulatio

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28

Morton, David P. "Stopping Rules for a Class of Sampling-Based Stochastic Programming Algorithms." Operations Research 46, no. 5 (1998): 710–18. http://dx.doi.org/10.1287/opre.46.5.710.

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29

Doucet, A., A. Logothetis, and V. Krishnamurthy. "Stochastic sampling algorithms for state estimation of jump Markov linear systems." IEEE Transactions on Automatic Control 45, no. 2 (2000): 188–202. http://dx.doi.org/10.1109/9.839943.

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30

Philpott, A. B., and V. L. de Matos. "Dynamic sampling algorithms for multi-stage stochastic programs with risk aversion." European Journal of Operational Research 218, no. 2 (2012): 470–83. http://dx.doi.org/10.1016/j.ejor.2011.10.056.

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31

Lu, Jianguang, Juan Tang, Bin Xing, and Xianghong Tang. "Stochastic Approximate Algorithms for Uncertain Constrained K-Means Problem." Mathematics 10, no. 1 (2022): 144. http://dx.doi.org/10.3390/math10010144.

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The k-means problem has been paid much attention for many applications. In this paper, we define the uncertain constrained k-means problem and propose a (1+ϵ)-approximate algorithm for the problem. First, a general mathematical model of the uncertain constrained k-means problem is proposed. Second, the random sampling properties of the uncertain constrained k-means problem are studied. This paper mainly studies the gap between the center of random sampling and the real center, which should be controlled within a given range with a large probability, so as to obtain the important sampling prope

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32

Todorov, Venelin, Valerij Dzhurov, and Ilian Tzvetkov. "A COMPARISON OF SEVERAL STOCHASTIC TECHNIQUES FOR COMPUTATION OF MULTIDIMENSIONAL INTEGRALS." Journal Scientific and Applied Research 20, no. 1 (2020): 11–18. http://dx.doi.org/10.46687/jsar.v20i1.299.

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A comprehensive experimental study based on Sobol sequence, Importance Sampling and Fibonacci based lattice rule has been done. This is the first time the Sobol sequence has been compared with the Importance sampling method for the problem under consideration. The numerical tests show that the stochastic algorithms under consideration are efficient tool for computing multidimensional integrals. In order to obtain a more accurate and reliable interpretation of the results this is very important.

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An, Dong, Noah Linden, Jin-Peng Liu, Ashley Montanaro, Changpeng Shao, and Jiasu Wang. "Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance." Quantum 5 (June 24, 2021): 481. http://dx.doi.org/10.22331/q-2021-06-24-481.

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Inspired by recent progress in quantum algorithms for ordinary and partial differential equations, we study quantum algorithms for stochastic differential equations (SDEs). Firstly we provide a quantum algorithm that gives a quadratic speed-up for multilevel Monte Carlo methods in a general setting. As applications, we apply it to compute expectation values determined by classical solutions of SDEs, with improved dependence on precision. We demonstrate the use of this algorithm in a variety of applications arising in mathematical finance, such as the Black-Scholes and Local Volatility models,

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34

Trovo, Francesco, Stefano Paladino, Marcello Restelli, and Nicola Gatti. "Sliding-Window Thompson Sampling for Non-Stationary Settings." Journal of Artificial Intelligence Research 68 (May 26, 2020): 311–64. http://dx.doi.org/10.1613/jair.1.11407.

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Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different f

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35

Todorov, Venelin. "MONTE CARLO SAMPLING TECHNIQUES FOR COMPUTATION OF MULTIDIMENSIONAL INTEGRALS RELATED TO MIGRATION." Journal Scientific and Applied Research 16, no. 1 (2019): 23–31. http://dx.doi.org/10.46687/jsar.v16i1.260.

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We study multidimensional integrals with applications to international migration forecasting. A comprehensive experimental study based on Latin Hypercube and Importance Sampling and Fibonacci based lattice rule has been done. This is the first time such a comparison has been made. The numerical tests show that the stochastic algorithms under consideration are efficient tool for computing multidimensional integrals. It is important in order to obtain a more accurate and reliable interpretation of the results which is a foundation in international migration forecasting.

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36

Schell, Thomas, та Stefan Wegenkittl. "Looking Beyond Selection Probabilities: Adaptation of the χ2 Measure for the Performance Analysis of Selection Methods in GAs". Evolutionary Computation 9, № 2 (2001): 243–56. http://dx.doi.org/10.1162/106365601750190424.

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Viewing the selection process in a genetic algorithm as a two-step procedure consisting of the assignment of selection probabilities and the sampling according to this distribution, we employ the χ2 measure as a tool for the analysis of the stochastic properties of the sampling. We are thereby able to compare different selection schemes even in the case that their probability distributions coincide. Introducing a new sampling algorithm with adjustable accuracy and employing two-level test designs enables us to further reveal the intrinsic correlation structures of well-known sampling algorithm

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37

He, Yandong, Zhong Zheng, Huilin Li, and Jie Deng. "A Stochastic Drone-Scheduling Problem with Uncertain Energy Consumption." Drones 8, no. 9 (2024): 430. http://dx.doi.org/10.3390/drones8090430.

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In this paper, we present a stochastic drone-scheduling problem where the energy consumption of drones between any two nodes is uncertain. Considering uncertain energy consumption as opposed to deterministic energy consumption can effectively enhance the safety of drone flights. To address this issue, we developed a two-stage stochastic programming model with recourse cost, and we employed a fixed-sample sampling strategy based on Monte Carlo simulation to characterize uncertain variables, followed by the design of an efficient variable neighborhood search algorithm to solve the model. Case st

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38

Wang, Peng, Ge Li, Yong Peng, and Rusheng Ju. "Random Finite Set Based Parameter Estimation Algorithm for Identifying Stochastic Systems." Entropy 20, no. 8 (2018): 569. http://dx.doi.org/10.3390/e20080569.

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Parameter estimation is one of the key technologies for system identification. The Bayesian parameter estimation algorithms are very important for identifying stochastic systems. In this paper, a random finite set based algorithm is proposed to overcome the disadvantages of the existing Bayesian parameter estimation algorithms. It can estimate the unknown parameters of the stochastic system which consists of a varying number of constituent elements by using the measurements disturbed by false detections, missed detections and noises. The models used for parameter estimation are constructed by

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39

Linowsky, K., and A. B. Philpott. "On the Convergence of Sampling-Based Decomposition Algorithms for Multistage Stochastic Programs." Journal of Optimization Theory and Applications 125, no. 2 (2005): 349–66. http://dx.doi.org/10.1007/s10957-004-1842-z.

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Zhang, Yuzhen, Jun Ma, Shunlin Liang, Xisheng Li, and Manyao Li. "An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products." Remote Sensing 12, no. 24 (2020): 4015. http://dx.doi.org/10.3390/rs12244015.

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This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optima

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41

Liu, Yizhi. "An investigation of progress related to stochastic stationary bandit algorithms." Applied and Computational Engineering 34, no. 1 (2024): 197–201. http://dx.doi.org/10.54254/2755-2721/34/20230326.

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The Multi-armed Bandit algorithm stands as a consequential tool for informed decision-making, distinct from reliance on intuitive selections, given its systematic proclivity to meticulously assess accessible alternatives with the intent of discerning the most auspicious outcome. Amid the repertoire of algorithmic variations, the Stochastic Stationary Bandit algorithm assumes a foundational and enduring role, finding versatile application across diverse domains, including but not limited to digital advertising, price optimization, and recommendation systems. With these considerations in view, t

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Zhang, Yanhui, and Wenyu Yang. "A comparative study of the stochastic simulation methods applied in structural health monitoring." Engineering Computations 31, no. 7 (2014): 1484–513. http://dx.doi.org/10.1108/ec-07-2013-0185.

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Purpose – The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM). Design/methodology/approach – On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algori

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Wang, Ming, Shou Jun Bai, and Huan Bao Wang. "A Novel Stochastic Localization Algorithm for Sensor Nodes in Wireless Sensor Networks." Applied Mechanics and Materials 39 (November 2010): 510–16. http://dx.doi.org/10.4028/www.scientific.net/amm.39.510.

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Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing

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Lim, Ming Chong, and Han-Lim Choi. "Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints." Applied Sciences 9, no. 10 (2019): 2117. http://dx.doi.org/10.3390/app9102117.

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Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated

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Zhou, Jin. "Application and comparative analysis of adaptive strategies in multi-armed bandit algorithms." Applied and Computational Engineering 64, no. 1 (2024): 237–48. http://dx.doi.org/10.54254/2755-2721/64/20241354.

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This study explores the application and comparative analysis of adaptive strategies in multi-armed bandit algorithms, specifically focusing on the -greedy algorithm, the Upper Confidence Bound (UCB) algorithm, and the Thompson sampling algorithm. By designing and implementing a series of experiments, the research identifies Thompson sampling as the most effective method, despite its greater reward fluctuation, highlighting its superior adaptability in uncertain environments. The comparative analysis reveals that each algorithm possesses distinct advantages and drawbacks, necessitating a strate

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Trainor-Guitton, Whitney, and G. Michael Hoversten. "Stochastic inversion for electromagnetic geophysics: Practical challenges and improving convergence efficiency." GEOPHYSICS 76, no. 6 (2011): F373—F386. http://dx.doi.org/10.1190/geo2010-0223.1.

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Traditional deterministic geophysical inversion algorithms are not designed to provide a robust evaluation of uncertainty that reflects the limitations of the geophysical technique. Stochastic inversions, which do provide a sampling-based measure of uncertainty, are computationally expensive and not straightforward to implement for nonexperts (nonstatisticians). Our results include stochastic inversion for magnetotelluric and controlled source electromagnetic data. Two Markov Chain sampling algorithms (Metropolis-Hastings and Slice Sampler) can significantly decrease the computational expense

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Cameron, Scott, Hans Eggers, and Steve Kroon. "A Sequential Marginal Likelihood Approximation Using Stochastic Gradients." Proceedings 33, no. 1 (2019): 18. http://dx.doi.org/10.3390/proceedings2019033018.

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Existing algorithms like nested sampling and annealed importance sampling are able to produce accurate estimates of the marginal likelihood of a model, but tend to scale poorly to large data sets. This is because these algorithms need to recalculate the log-likelihood for each iteration by summing over the whole data set. Efficient scaling to large data sets requires that algorithms only visit small subsets (mini-batches) of data on each iteration. To this end, we estimate the marginal likelihood via a sequential decomposition into a product of predictive distributions p ( y n | y < n ) . P

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Yang, Feng, Yujuan Luo, and Litao Zheng. "Double-Layer Cubature Kalman Filter for Nonlinear Estimation." Sensors 19, no. 5 (2019): 986. http://dx.doi.org/10.3390/s19050986.

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The cubature Kalman filter (CKF) has poor performance in strongly nonlinear systems while the cubature particle filter has high computational complexity induced by stochastic sampling. To address these problems, a novel CKF named double-Layer cubature Kalman filter (DLCKF) is proposed. In the proposed DLCKF, the prior distribution is represented by a set of weighted deterministic sampling points, and each deterministic sampling point is updated by the inner CKF. Finally, the update mechanism of the outer CKF is used to obtain the state estimations. Simulation results show that the proposed alg

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BEIGY, HAMID, and M. R. MEYBODI. "UTILIZING DISTRIBUTED LEARNING AUTOMATA TO SOLVE STOCHASTIC SHORTEST PATH PROBLEMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 14, no. 05 (2006): 591–615. http://dx.doi.org/10.1142/s0218488506004217.

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In this paper, we first introduce a network of learning automata, which we call it as distributed learning automata and then propose some iterative algorithms for solving stochastic shortest path problem. These algorithms use distributed learning automata to find a policy that determines a path from a source node to a destination node with minimal expected cost (length). In these algorithms, at each stage distributed learning automata determines which edges to be sampled. This sampling method may result in decreasing unnecessary samples and hence decreasing the running time of algorithms. It i

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Jose, Sharu Theresa, and Shana Moothedath. "Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis." Entropy 26, no. 7 (2024): 606. http://dx.doi.org/10.3390/e26070606.

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We study stochastic linear contextual bandits (CB) where the agent observes a noisy version of the true context through a noise channel with unknown channel parameters. Our objective is to design an action policy that can “approximate” that of a Bayesian oracle that has access to the reward model and the noise channel parameter. We introduce a modified Thompson sampling algorithm and analyze its Bayesian cumulative regret with respect to the oracle action policy via information-theoretic tools. For Gaussian bandits with Gaussian context noise, our information-theoretic analysis shows that unde

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