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Relevant bibliographies by topics / Probabilistic cost estimation

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Academic literature on the topic 'Probabilistic cost estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Probabilistic cost estimation"

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Jaafari, A. "Probabilistic unit cost estimation for project configuration optimization." International Journal of Project Management 6, no. 4 (1988): 226–34. http://dx.doi.org/10.1016/0263-7863(88)90007-5.

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Carpenter, Chris. "Probabilistic Cost and Time Estimation of Rigless Plug and Abandonment." Journal of Petroleum Technology 67, no. 01 (2015): 117–19. http://dx.doi.org/10.2118/0115-0117-jpt.

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Takenouchi, Takashi. "A Novel Parameter Estimation Method for Boltzmann Machines." Neural Computation 27, no. 11 (2015): 2423–46. http://dx.doi.org/10.1162/neco_a_00781.

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We propose a novel estimator for a specific class of probabilistic models on discrete spaces such as the Boltzmann machine. The proposed estimator is derived from minimization of a convex risk function and can be constructed without calculating the normalization constant, whose computational cost is exponential order. We investigate statistical properties of the proposed estimator such as consistency and asymptotic normality in the framework of the estimating function. Small experiments show that the proposed estimator can attain comparable performance to the maximum likelihood expectation at

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Lee, Jin Hyuk, Yangrok Choi, Hojune Ann, Sung Yeol Jin, Seung-Jung Lee, and Jung Sik Kong. "Maintenance Cost Estimation in PSCI Girder Bridges Using Updating Probabilistic Deterioration Model." Sustainability 11, no. 23 (2019): 6593. http://dx.doi.org/10.3390/su11236593.

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A deterioration model plays an important role to predict the valid total maintenance cost for sustainable maintenance of bridges. In the current state-of-the-art, the deterioration model has regression parameters as a probabilistic process by an initially determined mean and standard deviation, called an existing model. However, the existing model has difficulty to predict maintenance costs accurately, because it cannot reflect an information based on structural damage at an operational stage. In this research, updating the probabilistic deterioration model is presented for the prediction of p

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Guan, Zhenchang, Tao Deng, Yujing Jiang, Cheng Zhao, and Hongwei Huang. "Probabilistic estimation of ground condition and construction cost for mountain tunnels." Tunnelling and Underground Space Technology 42 (May 2014): 175–83. http://dx.doi.org/10.1016/j.tust.2014.02.014.

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Wang, Wei-Chih, Shih-Hsu Wang, Yu-Kun Tsui, and Ching-Hsiang Hsu. "A factor-based probabilistic cost model to support bid-price estimation." Expert Systems with Applications 39, no. 5 (2012): 5358–66. http://dx.doi.org/10.1016/j.eswa.2011.11.049.

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Mackie, Kevin R., John-Michael Wong, and Bozidar Stojadinovic. "Bridge Damage and Loss Scenarios Calibrated by Schematic Design and Cost Estimation of Repairs." Earthquake Spectra 27, no. 4 (2011): 1127–45. http://dx.doi.org/10.1193/1.3651362.

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In this study probabilistic seismic loss models for reinforced concrete bridges are improved with separate models for connecting damage to repair quantities and repair quantities to money and time costs. This approach allows explicit consideration of repair design and variability of cost and time estimating that are not captured using a direct relationship between damage and loss. The proposed repair and cost models require schematic designs of bridge repairs and cost estimations to determine the model parameters, which were completed for three scenarios of damage on a single bridge. These mod

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Ismail, Al Emran. "Probabilistic Analysis of Surface Crack in Round Bars under Tension Loading." Applied Mechanics and Materials 315 (April 2013): 655–59. http://dx.doi.org/10.4028/www.scientific.net/amm.315.655.

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This paper presents the probabilistic analysis of surface crack in round bars subjected to tension stress using Monte Carlo Simulation (MCS). A probabilistic model based on an elastic finite element analysis (FEA) was developed to evaluate the failure probability obtained using a K-estimation method. This method is based on the stress intensity factor (SIF) where the failure occurred when the SIF was assumed to exceed the critical SIF. It was found that, the K-estimation method was adequate to evaluate the failure probability of the bars when compared with the results obtained using the probab

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Rapaic, Danilo, Lidija Krstanovic, Nebojsa Ralevic, Ratko Obradovic, and Djuro Klipa. "Sparse regularized fuzzy regression." Applicable Analysis and Discrete Mathematics 13, no. 2 (2019): 583–604. http://dx.doi.org/10.2298/aadm171227021r.

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In this work, we focus on two things: First, in addition to the data measurement uncertainty, we develop a novel probabilistic model by imposing the additive noise in the classical fuzzy regression model. We obtain the baseline LS estimation as the maximum likelihood estimation for regression parameters. Moreover, by assuming the heavy tail distribution and by introducing the Huber norm instead of square in the cost function, we obtain more general robust fuzzy M-estimator, much more suitable for modeling the outliers often present in the data sets.

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Zhao, Shuhuan. "Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2056008. http://dx.doi.org/10.1142/s021800142056008x.

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Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for

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Dissertations / Theses on the topic "Probabilistic cost estimation"

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Poppe, Peter L. "A probabilistic cost estimation model for unexploded ordnance removal." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA372960.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1999.<br>"September 1999". Thesis advisor(s): Timothy P. Anderson. Includes bibliographical references (p. 105-107). Also Available online.

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Malmquist, Daniel. "A probabilistic pricing model for a company's projects." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1099.

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The company’s pricing is often highly impacted by the estimation of competitors’ project costs, which also is the main scope in this degree project. The purpose is to develop a pricing model dealing with uncertainties, since this is a main issue in the current pricing process. A pre-study has been performed, followed by a model implementation. An analysis of the model was then made, before conclusions were drawn. Project cost estimation foremost, but also probability distribution functions and pricing as a general concept, were investigated in the mainly literary pre-study. Two suitable method

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Khachatryan, Andranik [Verfasser], and K. [Akademischer Betreuer] Böhm. "Clustering-Initialized Adaptive Histograms and Probabilistic Cost Estimation for Query Optimization / Andranik Khachatryan. Betreuer: K. Böhm." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1027141714/34.

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Slaga, Joshua J. "Evaluating the Cost of Sewer Disposal to Other Alternatives for the Management of Truck Wash Water Generated During Winter Maintenance Activities." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1396817103.

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Lazo-Langner, Alejandro. "Comparing strategies for thromboprophylaxis in major orthopedic surgery using an estimation of net risk-benefit through probabilistic simulation A clinical cost-effectiveness study." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27874.

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Clinical decisions should take into account the clinical risk and benefit of a new intervention compared to the reference treatment. A method was developed to compare multiple competing interventions using a clinical cost-effectiveness approach. A meta-analysis was conducted to estimate the clinical cost (major bleeding) and benefit (averted venous thromboembolism) of thromboprophylaxis with different anticoagulants in orthopedic surgery. The increment in cost and benefit of anticoagulants compared to placebo were calculated using replications of the values obtained through Monte Carlo simulat

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Schell, Kristen R. "Computational Models for Renewable Energy Target Achievement & Policy Analysis." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/735.

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To date, over 84% of countries worldwide have renewable energy targets (RET), requiring that a certain amount of electricity be produced from renewable sources by a target date. Despite the worldwide prevalence of these policies, little research has been conducted on ex-ante RET policy analysis. In an effort to move toward evidence-based policymaking, this thesis develops computational models to assess the tradeoffs associated with alternatives for both RET achievement and RET policy formulation, including the option of creating renewable energy credit (REC) markets to facilitate meeting an RE

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Asiedu, Yaw. "Life-cycle cost analysis and probabilistic cost estimating in engineering design using an air duct design case study." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0033/NQ63835.pdf.

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Valdes, Machado Alejandro. "Uncertainty Quantification and Calibration in Well Construction Cost Estimates." Thesis, 2013. http://hdl.handle.net/1969.1/151343.

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The feasibility and success of petroleum development projects depend to a large degree on well construction costs. Well construction cost estimates often contain high levels of uncertainty. In many cases, these costs have been estimated using deterministic methods that do not reliably account for uncertainty, leading to biased estimates. The primary objective of this work was to improve the reliability of deterministic well construction cost estimates by incorporating probabilistic methods into the estimation process. The method uses historical well cost estimates and actual well costs to deve

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Books on the topic "Probabilistic cost estimation"

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A Probabilistic Cost Estimation Model for Unexploded Ordnance Removal. Storming Media, 1999.

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Book chapters on the topic "Probabilistic cost estimation"

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Frangopol, Dan M., and Ahmed Banafa. "Incorporating Uncertainty into Cost Estimation at the Time of Preliminary Design." In Probabilistic Safety Assessment and Management ’96. Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3409-1_44.

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Lu, Tyler, and Craig Boutilier. "Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs." In Algorithmic Decision Theory. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24873-3_11.

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Zhu, Qiang, S. Motheramgari, and Yu Sun. "Cost Estimation for Large Queries via Fractional Analysis and Probabilistic Approach in Dynamic Multidatabase Environments." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44469-6_48.

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Misra, Janardan. "Towards a Framework for Collaborative Enterprise Security." In Threats, Countermeasures, and Advances in Applied Information Security. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0978-5.ch016.

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The role of human behaviour in enterprise security is one of the little studied aspects. The author proposes a reinforcement model of collaborative security employing basic concepts from game theory, socio-psychology, and probabilistic model-checking. The proposed model aims towards solving the problem of inducing positive network effect to enable user centric monitoring of security violations, in particular, against violations related to ”semantic manipulation” of context dependent logical resources. Preventing such violations using existing security enforcement mechanisms is neither feasible nor cost effective. The author defines a payoff mechanism to formalize the model by stipulating appropriate payoffs as reward, punishment, and community price according to reporting of genuine or false violations, non-reporting of the detected violations, and proactive reporting of vulnerabilities and threats by the users. Correctness properties of the model are defined in terms of probabilistic robustness property and constraints for economic feasibility of the payoffs. For estimating the payoff parameters, system and user behaviours are further modelled in terms of probabilistic finite state machines (PFSM) and likelihood of the success of the model is specified using probabilistic computation tree logic (PCTL). PRISM model checker based automated quantitative analysis elicits the process of the estimation of various parameters in the model using PFSMs and PCTL formulas.

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Geramifard, Omid, Jian-Xin Xu, and Junhong Zhou. "A Temporal Probabilistic Approach for Continuous Tool Condition Monitoring." In Diagnostics and Prognostics of Engineering Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2095-7.ch011.

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In this chapter, a temporal probabilistic approach based on hidden semi-Markov model is proposed for continuous (real-valued) tool condition monitoring in machinery systems. As an illustrative example, tool wear prediction in CNC-milling machine is conducted using the proposed approach. Results indicate that the additional flexibility provided in the new approach compared to the existing hidden Markov model-based approach improves the performance. 482 features are extracted from 7 signals (three force signals, three vibration signals and acoustic emission) that are acquired for each experiment. After the feature extraction phase, Fisher’s discriminant ratio is applied to find the most discriminant features to construct the prediction model. The prediction results are provided for three different cases, i.e. cross-validation, diagnostics, and prognostics. The possibility of incorporating an asymmetric loss function in the proposed approach in order to reflect and consider the cost differences between an under- and over-estimation in tool condition monitoring is also explored and the simulation results are provided.

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Monizza, Gabriele Pasetti, Christoph Paul Schimanski, Giada Malacarne, and Dominik T. Matt. "BIM Simulation Lab." In Advances in Civil and Industrial Engineering. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7091-3.ch006.

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The architecture, engineering, and construction (AEC) sector is facing the digital transformation by promoting the building information modelling (BIM) as a standard methodology for the digital managing of information along the whole lifecycle of a construction work. Although small-medium enterprises (SMEs) and public administrations (PAs) are aware of the BIM benefit, they ask for pilot actions and tools for testing BIM applications in their daily activities in order to measure benefits and difficulties in detail. This chapter discusses the BIM Simulation Lab initiative which aims at establishing a laboratory for an effective and efficient BIM implementation, by promoting a physical space and specific services for supporting the territory in the DT. The authors describe the concept of the lab and they introduce an assessment method that adopts an indirect approach of the BIM benefit assessment leveraging principles from construction cost estimation and probabilistic risk management.

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Posner, Richard A. "Conclusion." In Catastrophe. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195178135.003.0008.

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To summarize very briefly: The risks of global catastrophe are greater and more numerous than is commonly supposed, and they are growing, probably rapidly. They are growing for several reasons: the increasing rate of technological advance—for a number of the catastrophic risks are created or exacerbated by science and its technological and industrial applications (including such humble ones as the internal combustion engine); the growth of the world economy and world population (both, in part, moreover, indirect consequences of technological progress); and the rise of apocalyptic global terrorism. And the risks are, to a degree, convergent or mutually reinforcing. For example, global warming contributes to loss of biodiversity, an asteroid collision could precipitate catastrophic global warming and cause mass extinctions, and cyberterrorism could be employed to facilitate terrorist attacks with weapons of mass destruction. Each catastrophic risk, being slight in a probabilistic sense (or seeming slight, because often the probability cannot be estimated even roughly) when the probability is computed over a relatively short time span, such as a year or even a decade, is difficult for people to take seriously. Apart from the psychological difficulty that people have in thinking in terms of probabilities rather than frequencies, frequencies normally provide a better grounding for estimating probabilities than theory does; frequent events generate information that enables probabilities to be confirmed or updated. The fact that there have been both nuclear attacks and, albeit on a very limited scale, bioterrorist attacks—which, however, resemble natural disease episodes, of which the human race has a long experience—has enabled the public to take these particular risks seriously. The general tendency, however, is to ignore the catastrophic risks, both individually and in the aggregate. Economic, political, and cultural factors, including the religious beliefs prevalent in the United States, reinforce the effect of cognitive factors (including information costs) in inducing neglect of such risks. The neglect is misguided. The expected costs of even very-low-probability events can be huge if the adverse consequences should the probability materialize are huge, or if the interval over which the probability is estimated is enlarged; the risk of a catastrophic collision with an asteroid is slight in the time span of a year, but not so slight in the time span of a hundred years.

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Conference papers on the topic "Probabilistic cost estimation"

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Hajimiri, Hadi, Kamran Rahmani, and Prabhat Mishra. "Efficient Peak Power Estimation Using Probabilistic Cost-Benefit Analysis." In 2015 28th International Conference on VLSI Design (VLSI 2015) and 14th International Conference on Embedded Systems. IEEE, 2015. http://dx.doi.org/10.1109/vlsid.2015.68.

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Phannachitta, Passakorn, Jacky Keung, Akito Monden, and Ken-ichi Matsumoto. "Improving Analogy-Based Software Cost Estimation through Probabilistic-Based Similarity Measures." In 2013 20th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2013. http://dx.doi.org/10.1109/apsec.2013.78.

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Carrese, Stefano, and Paolo Strazzullo. "A Probabilistic Model for the Maintenance Cost Estimation of a Railway Fleet." In International Conference on Traffic and Transportation Studies (ICTTS) 2002. American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40630(255)203.

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Loberg, Tove, Oystein Arild, Antonino Merlo, and Paolo D'Alesio. "The How's and Why's of Probabilistic Well Cost Estimation." In IADC/SPE Asia Pacific Drilling Technology Conference and Exhibition. Society of Petroleum Engineers, 2008. http://dx.doi.org/10.2118/114696-ms.

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Timmons, Eric, and Brian C. Williams. "Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/721.

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State estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing belief states for real world systems, however they have difficulty scaling to systems with more than a handful of components. Classical, consistency based diagnosis methods scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts summarize constraint violations, but probabilistic hybrid estimation is relativel

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Ryan, Allison D., Hugh Durrant-Whyte, and J. Karl Hedrick. "Information-Theoretic Sensor Motion Control for Distributed Estimation." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-43791.

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Estimate uncertainty is a clear metric for sensing problems, but is not traditionally used for optimal control of mobile sensors because it is difficult to model how it is affected by sensor motion. This work develops a multiple-step receding horizon cost for sensor motion control based on minimization of expected entropy of the estimate distribution. The structure of the cost function is analyzed and used to upper bound the degree of coupling between sensors. The contribution is a multiple step prediction of the estimate entropy incorporating probabilistic sensor and target motion models and

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Golden, Patrick J., Sushant K. Jha, and James M. Larsen. "Confidence Bound Estimation for Mechanism-Based Small Crack Growth Probabilistic Design Life Predictions." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91860.

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Abstract Current design lives for US Air Force turbine engine materials are based on a 1 in 1000 rate of nucleation of an engineering sized crack (B0.1). These lives are determined from models fitted to test coupon fatigue data at many different loading conditions. It has been shown that this methodology can sometimes lead to excess conservatism, and it often does not fully incorporate understanding of the mechanisms that drive crack initiation, growth, and fracture. A mechanism based probabilistic life forecasting methodology has been previously proposed with the objective to improve the pred

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Moeinikia, Fatemeh, Kjell Kare Fjelde, Arild Saasen, and Torbjorn Vralstad. "An Investigation of Different Approaches for Probabilistic Cost and Time Estimation of Rigless P&A in Subsea Multi-Well Campaign." In SPE Bergen One Day Seminar. Society of Petroleum Engineers, 2014. http://dx.doi.org/10.2118/169203-ms.

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Wang, Ruohui, and Dahua Lin. "Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/646.

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We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the

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Zhang, Zhipeng, Kang Zhou, and Xiang Liu. "Probabilistic Risk Analysis of Broken Rail-Caused Train Derailments." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8100.

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Abstract Broken-rail prevention and risk management have been being a major activity for a long time for the railroad industry. The major objective of this research is to evaluate and analyze the broken rail-caused derailment risk using Artificial Intelligence (AI) approaches. The risk model is primarily built upon 1) broken rail probability; 2) probability of broken-rail derailment given a broken rail; and 3) derailment severity, measured by the number of cars derailed. The train derailment risk accounts for derailment probability and derailment consequences simultaneously. Due to the low fre

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Reports on the topic "Probabilistic cost estimation"

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Fu, Gongkang. Evaluation of Illinois Bridge Deterioration Models. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-029.

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The National Bridge Inventory bridge inspection system ranks the condition of bridge components on a scale of zero to nine. The resulting condition ratings represent an important element considered in deciding measures for bridge maintenance, repair, and rehabilitation. Thus, forecasting future condition ratings well is critical to reliable planning for these activities and estimating the costs. The Illinois Department of Transportation currently has deterministic models for this purpose. This study’s objective is to review the current models using condition rating histories gathered from 1980

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