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Journal articles on the topic 'Bayesian belief'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Marrero, Osvaldo. "What is Bayesian statistics?" Mathematical Gazette 100, no. 548 (June 14, 2016): 247–56. http://dx.doi.org/10.1017/mag.2016.61.

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Bayesian statistics is included in few elementary statistics courses, and many mathematicians have heard of it, perhaps through collateral readings from popular literature or [1], selected as an Editor's Choice in the New York Times Book Review. ‘Bayesian statistics’ provides for a way to incorporate prior beliefs, experience, or information into the analysis of data. Bayesian thinking is natural, and that is an advantage. For example, on a summer morning, if we see dark rain clouds up in the sky, we leave home for work with an umbrella because prior experience tells us that doing so is beneficial. In general, the idea is simple; schematically, it looks like this:(prior belief) + (data: new information) ⇒ (posterior belief).Thus, we begin with a prior belief that we allow to be modified or informed by new data to produce a posterior belief, which then becomes our new prior, and this process is never-ending. We are always willing to update our beliefs according to new information.

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2

Dietrich, Franz. "Bayesian group belief." Social Choice and Welfare 35, no. 4 (April 29, 2010): 595–626. http://dx.doi.org/10.1007/s00355-010-0453-x.

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3

LIN, YAN, and MAREK J. DRUZDZEL. "RELEVANCE-BASED INCREMENTAL BELIEF UPDATING IN BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 02 (March 1999): 285–95. http://dx.doi.org/10.1142/s0218001499000161.

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Relevance reasoning in Bayesian networks can be used to improve efficiency of belief updating algorithms by identifying and pruning those parts of a network that are irrelevant for computation. Relevance reasoning is based on the graphical property of d-separation and other simple and efficient techniques, the computational complexity of which is usually negligible when compared to the complexity of belief updating in general. This paper describes a belief updating technique based on relevance reasoning that is applicable in practical systems in which observations and model revisions are interleaved with belief updating. Our technique invalidates the posterior beliefs of those nodes that depend probabilistically on the new evidence or the revised part of the model and focuses the subsequent belief updating on the invalidated beliefs rather than on all beliefs. Very often observations and model updating invalidate only a small fraction of the beliefs and our scheme can then lead to sub stantial savings in computation. We report results of empirical tests for incremental belief updating when the evidence gathering is interleaved with reasoning. These tests demonstrate the practical significance of our approach.

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4

Augenblick, Ned, and Matthew Rabin. "Belief Movement, Uncertainty Reduction, and Rational Updating*." Quarterly Journal of Economics 136, no. 2 (February 3, 2021): 933–85. http://dx.doi.org/10.1093/qje/qjaa043.

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Abstract When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

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5

Chambers, Christopher P., and Takashi Hayashi. "Bayesian consistent belief selection." Journal of Economic Theory 145, no. 1 (January 2010): 432–39. http://dx.doi.org/10.1016/j.jet.2009.07.001.

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6

Tang, Qianfeng. "Hierarchies of beliefs and the belief-invariant Bayesian solution." Journal of Mathematical Economics 59 (August 2015): 111–16. http://dx.doi.org/10.1016/j.jmateco.2015.06.006.

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7

Liu, Qingmin. "Stability and Bayesian Consistency in Two-Sided Markets." American Economic Review 110, no. 8 (August 1, 2020): 2625–66. http://dx.doi.org/10.1257/aer.20181186.

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We propose a criterion of stability for two-sided markets with asymmetric information. A central idea is to formulate off-path beliefs conditional on counterfactual pairwise deviations and on-path beliefs in the absence of such deviations. A matching-belief configuration is stable if the matching is individually rational with respect to the system of on-path beliefs and is not blocked with respect to the system of off-path beliefs. The formulation provides a language for assessing matching outcomes with respect to their supporting beliefs and opens the door to further belief-based refinements. The main refinement analyzed in the paper requires the Bayesian consistency of on-path and off-path beliefs with prior beliefs. We define concepts of Bayesian efficiency, the rational expectations competitive equilibrium, and the core. Their contrast with pairwise stability manifests the role of information asymmetry in matching formation. (JEL C78, D40, D82, D83)

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8

Shek, T. W. "Bayesian Belief Network in histopathology." Journal of Clinical Pathology 49, no. 10 (October 1, 1996): 864. http://dx.doi.org/10.1136/jcp.49.10.864-b.

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9

Jaffray, J. Y. "Bayesian updating and belief functions." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 5 (1992): 1144–52. http://dx.doi.org/10.1109/21.179852.

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10

Pinheiro de Cristo, Marco Antônio, Pável Pereira Calado, Maria de Lourdes da Silveira, Ilmério Silva, Richard Muntz, and Berthier Ribeiro-Neto. "Bayesian belief networks for IR." International Journal of Approximate Reasoning 34, no. 2-3 (November 2003): 163–79. http://dx.doi.org/10.1016/j.ijar.2003.07.006.

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11

Dave, Upendra, and A. Gammeron. "Probabilistic Reasoning and Bayesian Belief Networks." Journal of the Operational Research Society 47, no. 5 (May 1996): 721. http://dx.doi.org/10.2307/3010031.

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12

Herskovits, E. H., and G. F. Cooper. "Algorithms for Bayesian Belief-Network Precomputation." Methods of Information in Medicine 30, no. 02 (1991): 81–89. http://dx.doi.org/10.1055/s-0038-1634820.

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AbstractBayesian belief networks provide an intuitive and concise means of representing probabilistic relationships among the variables in expert systems. A major drawback to this methodology is its computational complexity. We present an introduction to belief networks, and describe methods for precomputing, or caching, part of a belief network based on metrics of probability and expected utility. These algorithms are examples of a general method for decreasing expected running time for probabilistic inference.We first present the necessary background, and then present algorithms for producing caches based on metrics of expected probability and expected utility. We show how these algorithms can be applied to a moderately complex belief network, and present directions for future research.

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13

Dave, Upendra. "Probabilistic Reasoning and Bayesian Belief Networks." Journal of the Operational Research Society 47, no. 5 (May 1996): 721–22. http://dx.doi.org/10.1057/jors.1996.92.

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14

Baron, Dror, Shriram Sarvotham, and Richard G. Baraniuk. "Bayesian Compressive Sensing Via Belief Propagation." IEEE Transactions on Signal Processing 58, no. 1 (January 2010): 269–80. http://dx.doi.org/10.1109/tsp.2009.2027773.

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15

de CAMPOS, LUIS M., JOSE A. GÁMEZ, and SERAFÍN MORAL. "SIMPLIFYING EXPLANATIONS IN BAYESIAN BELIEF NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, no. 04 (August 2001): 461–89. http://dx.doi.org/10.1142/s0218488501000892.

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Abductive inference in Bayesian belief networks is intended as the process of generating the K most probable configurations given an observed evidence. These configurations are called explanations and in most of the approaches found in the literature, all the explanations have the same number of literals. In this paper we propose some criteria to simplify the explanations in such a way that the resulting configurations are still accounting for the observed facts. Computational methods to perform the simplification task are also presented. Finally the algorithms are experimentally tested using a set of experiments which involves three different Bayesian belief networks.

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16

Viscusi, W. Kip, and Wesley A. Magat. "Bayesian decisions with ambiguous belief aversion." Journal of Risk and Uncertainty 5, no. 4 (October 1992): 371–87. http://dx.doi.org/10.1007/bf00122576.

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17

van der Gaag, L. C. "Bayesian Belief Networks: Odds and Ends." Computer Journal 39, no. 2 (February 1, 1996): 97–113. http://dx.doi.org/10.1093/comjnl/39.2.97.

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18

Xu, Jian-min, Shu-fang Wu, and Yu Hong. "Topic tracking with Bayesian belief network." Optik 125, no. 9 (May 2014): 2164–69. http://dx.doi.org/10.1016/j.ijleo.2013.10.044.

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19

Hassall, Kirsty L., Gordon Dailey, Joanna Zawadzka, Alice E. Milne, Jim A. Harris, Ron Corstanje, and Andrew P. Whitmore. "Facilitating the elicitation of beliefs for use in Bayesian Belief modelling." Environmental Modelling & Software 122 (December 2019): 104539. http://dx.doi.org/10.1016/j.envsoft.2019.104539.

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20

Hill, Brian. "CONFIDENCE IN BELIEFS AND RATIONAL DECISION MAKING." Economics and Philosophy 35, no. 02 (October 30, 2018): 223–58. http://dx.doi.org/10.1017/s0266267118000214.

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Abstract:The standard, Bayesian account of rational belief and decision is often argued to be unable to cope properly with severe uncertainty, of the sort ubiquitous in some areas of policy making. This paper tackles the question of what should replace it as a guide for rational decision making. It defends a recent proposal, which reserves a role for the decision maker’s confidence in beliefs. Beyond being able to cope with severe uncertainty, the account has strong normative credentials on the main fronts typically evoked as relevant for rational belief and decision. It fares particularly well, we argue, in comparison to other prominent non-Bayesian models in the literature.

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21

MacGilchrist, Renaud S., and Julia Roloff. "A Bayesian Belief Network Exploring CSP Relationships." Academy of Management Proceedings 2015, no. 1 (January 2015): 16323. http://dx.doi.org/10.5465/ambpp.2015.16323abstract.

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22

Lewis, Nigel D. C. "Continuous process improvement using Bayesian belief networks." Computers & Industrial Engineering 37, no. 1-2 (October 1999): 449–52. http://dx.doi.org/10.1016/s0360-8352(99)00115-1.

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23

Raphael, Christopher. "Synthesizing Musical Accompaniments With Bayesian belief networks." Journal of New Music Research 30, no. 1 (March 1, 2001): 59–67. http://dx.doi.org/10.1076/jnmr.30.1.59.7121.

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24

Sprenger, Jan. "Conditional Degree of Belief and Bayesian Inference." Philosophy of Science 87, no. 2 (April 1, 2020): 319–35. http://dx.doi.org/10.1086/707554.

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25

Bennett, D. "The Neural Mechanisms of Bayesian Belief Updating." Journal of Neuroscience 35, no. 50 (December 16, 2015): 16300–16302. http://dx.doi.org/10.1523/jneurosci.3742-15.2015.

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26

Dagum, P., and R. M. Chavez. "Approximating probabilistic inference in Bayesian belief networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 15, no. 3 (March 1993): 246–55. http://dx.doi.org/10.1109/34.204906.

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27

van Engelen, R. A. "Approximating Bayesian belief networks by arc removal." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 8 (1997): 916–20. http://dx.doi.org/10.1109/34.608295.

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28

Brown, William, Zalán Gyenis, and Miklós Rédei. "The Modal Logic of Bayesian Belief Revision." Journal of Philosophical Logic 48, no. 5 (December 3, 2018): 809–24. http://dx.doi.org/10.1007/s10992-018-9495-9.

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29

Snow1, Paul. "AN INTUITIVE MOTIVATION OF BAYESIAN BELIEF MODELS." Computational Intelligence 11, no. 3 (August 1995): 449–59. http://dx.doi.org/10.1111/j.1467-8640.1995.tb00044.x.

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30

Nyberg, J. Brian, Bruce G. Marcot, and Randy Sulyma. "Using Bayesian belief networks in adaptive management." Canadian Journal of Forest Research 36, no. 12 (December 1, 2006): 3104–16. http://dx.doi.org/10.1139/x06-108.

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Bayesian belief and decision networks are modelling techniques that are well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes, from illustrating a conceptual understanding of system relations to calculating joint probabilities for decision options and predicting outcomes of management policies. We describe the nature and capabilities of BBNs, discuss their applications to the adaptive-management process, and present a case example of adaptive management of forests and terrestrial lichens in north-central British Columbia. We recommend that those unfamiliar with BBNs should begin by first developing influence diagrams with relatively simple structures that represent the system under management. Such basic models can then be elaborated to include more variables, the mathematical relations among them, and features that allow assessment of the utility of alternative management actions or strategies. Users of BBNs should be aware of several important limitations, including problems in representing feedback and time–dynamic functions. Nevertheless, when properly used, Bayesian networks can benefit most adaptive-management teams by promoting a shared understanding of the system being managed and encouraging the rigorous examination of alternative management policies.

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31

Olson, John T., Jerzy W. Rozenblit, Claudio Talarico, and Witold Jacak. "Hardware/Software Partitioning Using Bayesian Belief Networks." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 37, no. 5 (September 2007): 655–68. http://dx.doi.org/10.1109/tsmca.2007.902623.

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32

Dietrich, Franz, and Christian List. "Reasons for (prior) belief in Bayesian epistemology." Synthese 190, no. 5 (February 27, 2013): 787–808. http://dx.doi.org/10.1007/s11229-012-0224-6.

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33

Basu, Pathikrit. "Bayesian updating rules and AGM belief revision." Journal of Economic Theory 179 (January 2019): 455–75. http://dx.doi.org/10.1016/j.jet.2018.11.005.

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34

Meilă, Marina, and Tommi Jaakkola. "Tractable Bayesian learning of tree belief networks." Statistics and Computing 16, no. 1 (January 2006): 77–92. http://dx.doi.org/10.1007/s11222-006-5535-3.

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35

Lampis, M., and J. D. Andrews. "Bayesian belief networks for system fault diagnostics." Quality and Reliability Engineering International 25, no. 4 (November 4, 2008): 409–26. http://dx.doi.org/10.1002/qre.978.

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36

Mardia, K. V. "Bayesian Image Analysis." Journal of Theoretical Medicine 1, no. 1 (1997): 63–77. http://dx.doi.org/10.1080/10273669708833007.

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Bayes' theorem is a vehicle for incorporating prior knowledge in updating the degree of belief in light of data. For example, the state of tomorrow's weather can be predicted using belief or likelihood of tomorrow's weather given today's weather data. We give a brief review of the recent advances in the area with emphasis on high-level Bayesian image analysis. It has been gradually recognised that knowledge-based algorithms based on Bayesian analysis are more widely applicable and reliable than ad hoc algorithms. Advantages include the use of explicit and realistic statistic models making it easier to understand the working behind such algorithms and allowing confidence statements to be made about conclusions. These systems are not necessarily as time consuming as might be expected. However, more care is required in using the knowledge effectively for a given specific problem; this is very much an art rather than a science.

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37

Wei, Lijian, and Lei Shi. "Investor Sentiment in an Artificial Limit Order Market." Complexity 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/8581793.

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This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

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38

Poirier, Dale J. "Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics." Journal of Economic Perspectives 2, no. 1 (February 1, 1988): 121–44. http://dx.doi.org/10.1257/jep.2.1.121.

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I plan to discuss, in as simple and nontechnical a fashion as possible, the subjectivist-Bayesian attitude toward model building in econometrics and to contrast it with the standard frequentist attitude. To convey what I believe is the principle distinguishing attitude between Bayesians and non-Bayesians, I refer to their respective positions as “subjectivist” and “frequentist.” The basic differences between these positions arise from different interpretations of “probability.” Frequentists interpret probability as a property of the external world, i.e., the limiting relative frequency of the occurrence of an event as the number of suitably defined trials goes to infinity. For a subjectivist, probability is interpreted as a degree of belief fundamentally internal to the individual as opposed to some characteristic of the external world. Subjective probability measures a relationship between the observer and events (not necessarily “repetitive”) of the outside world, expressing the observer's personal uncertainty about those events. The subjectivist paradigm is designed to produce “coherent” revisions in beliefs about future observables in light of observed data. Most of the issues I raise are familiar to statisticians but not to economists. Rather than give the suspicious reader a menu of Bayesian techniques, I hope to create an interest in acquiring a taste for the Bayesian cuisine by recommending five pragmatic principles.

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39

PENG, YUN, SHENYONG ZHANG, and RONG PAN. "BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 05 (October 2010): 539–64. http://dx.doi.org/10.1142/s0218488510006696.

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This paper investigates the problem of belief update in Bayesian networks (BN) with uncertain evidence. Two types of uncertain evidences are identified: virtual evidence (reflecting the uncertainty one has about a reported observation) and soft evidence (reflecting the uncertainty of an event one observes). Each of the two types of evidence has its own characteristics and obeys a belief update rule that is different from hard evidence, and different from each other. The particular emphasis is on belief update with multiple uncertain evidences. Efficient algorithms for BN reasoning with consistent and inconsistent uncertain evidences are developed, and their convergences analyzed. These algorithms can be seen as combining the techniques of traditional BN reasoning, Pearl's virtual evidence method, Jeffrey's rule, and the iterative proportional fitting procedure.

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40

BEDDOR, BOB, and SIMON GOLDSTEIN. "BELIEVING EPISTEMIC CONTRADICTIONS." Review of Symbolic Logic 11, no. 1 (August 8, 2017): 87–114. http://dx.doi.org/10.1017/s1755020316000514.

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AbstractWhat is it to believe something might be the case? We develop a puzzle that creates difficulties for standard answers to this question. We go on to propose our own solution, which integrates a Bayesian approach to belief with a dynamic semantics for epistemic modals. After showing how our account solves the puzzle, we explore a surprising consequence: virtually all of our beliefs about what might be the case provide counterexamples to the view that rational belief is closed under logical implication.

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41

Yershov, S. V., and F. V. Kostukevich. "Modeling technology based on fuzzy object-oriented Bayesian belief networks." PROBLEMS IN PROGRAMMING, no. 2-3 (June 2016): 179–87. http://dx.doi.org/10.15407/pp2016.02-03.179.

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The basic components of information technology inductive modeling causation under uncertainty based on fuzzy object-oriented Bayesian networks is proposed. The technology is based on a combination of transformation algorithms Bayesian network in the junction tree. New more efficient algorithms for Bayesian network transformation are resulted from modifications known algorithms; algorithms based on the use of more information on the graphical representation of the network are considered. Structurally functional model are described, it is designed to implement the transformation of fuzzy object-oriented Bayesian networks.

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42

WAKKER, PETER P. "DEMPSTER BELIEF FUNCTIONS ARE BASED ON THE PRINCIPLE OF COMPLETE IGNORANCE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 03 (June 2000): 271–84. http://dx.doi.org/10.1142/s0218488500000198.

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This paper shows that a "principle of complete ignorance" plays a central role in decisions based on Dempster belief functions. Such belief functions occur when, in a first stage, a random message is received and then, in a second stage, a true state of nature obtains. The uncertainty about the random message in the first stage is assumed to be probabilized, in agreement with the Bayesian principles. For the uncertainty in the second stage no probabilities are given. The Bayesian and belief function approaches part ways in the processing of the uncertainty in the second stage. The Bayesian approach requires that this uncertainty also be probabilized, which may require a resort to subjective information. Belief functions follow the principle of complete ignorance in the second stage, which permits strict adherence to objective inputs.

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43

Grimm, Veronika, and Friederike Mengel. "Experiments on Belief Formation in Networks." Journal of the European Economic Association 18, no. 1 (October 9, 2018): 49–82. http://dx.doi.org/10.1093/jeea/jvy038.

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Abstract We study belief formation in social networks using a laboratory experiment. Participants in our experiment observe an imperfect private signal on the state of the world and then simultaneously and repeatedly guess the state, observing the guesses of their network neighbors in each period. Across treatments we vary the network structure and the amount of information participants have about the network. Our first result shows that information about the network structure matters and in particular affects the share of correct guesses in the network. This is inconsistent with the widely used naive (deGroot) model. The naive model is, however, consistent with a larger share of individual decisions than the competing Bayesian model, whereas both models correctly predict only about 25%–30% of consensus beliefs. We then estimate a larger class of models and find that participants do indeed take network structure into account when updating beliefs. In particular they discount information from neighbors if it is correlated, but in a more rudimentary way than a Bayesian learner would.

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44

Gmytrasiewicz, Piotr. "How to Do Things with Words: A Bayesian Approach." Journal of Artificial Intelligence Research 68 (August 17, 2020): 753–76. http://dx.doi.org/10.1613/jair.1.11951.

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Communication changes the beliefs of the listener and of the speaker. The value of a communicative act stems from the valuable belief states which result from this act. To model this we build on the Interactive POMDP (IPOMDP) framework, which extends POMDPs to allow agents to model others in multi-agent settings, and we include communication that can take place between the agents to formulate Communicative IPOMDPs (CIPOMDPs). We treat communication as a type of action and therefore, decisions regarding communicative acts are based on decision-theoretic planning using the Bellman optimality principle and value iteration, just as they are for all other rational actions. As in any form of planning, the results of actions need to be precisely specified. We use the Bayes’ theorem to derive how agents update their beliefs in CIPOMDPs; updates are due to agents’ actions, observations, messages they send to other agents, and messages they receive from others. The Bayesian decision-theoretic approach frees us from the commonly made assumption of cooperative discourse – we consider agents which are free to be dishonest while communicating and are guided only by their selfish rationality. We use a simple Tiger game to illustrate the belief update, and to show that the ability to rationally communicate allows agents to improve efficiency of their interactions.

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45

GYENIS, ZALÁN. "STANDARD BAYES LOGIC IS NOT FINITELY AXIOMATIZABLE." Review of Symbolic Logic 13, no. 2 (March 22, 2019): 326–37. http://dx.doi.org/10.1017/s1755020319000157.

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AbstractIn the article [2] a hierarchy of modal logics has been defined to capture the logical features of Bayesian belief revision. Elements in that hierarchy were distinguished by the cardinality of the set of elementary propositions. By linking the modal logics in the hierarchy to the modal logics of Medvedev frames it has been shown that the modal logic of Bayesian belief revision determined by probabilities on a finite set of elementary propositions is not finitely axiomatizable. However, the infinite case remained open. In this article we prove that the modal logic of Bayesian belief revision determined by standard Borel spaces (these cover probability spaces that occur in most of the applications) is also not finitely axiomatizable.

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46

Sy, Bon K. "A recurrence local computation approach towards ordering composite beliefs in bayesian belief networks." International Journal of Approximate Reasoning 8, no. 1 (January 1993): 17–50. http://dx.doi.org/10.1016/s0888-613x(05)80004-0.

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47

Gilboa, Itzhak, Andrew W. Postlewaite, and David Schmeidler. "Probability and Uncertainty in Economic Modeling." Journal of Economic Perspectives 22, no. 3 (July 1, 2008): 173–88. http://dx.doi.org/10.1257/jep.22.3.173.

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Economic modeling assumes, for the most part, that agents are Bayesian, that is, that they entertain probabilistic beliefs, objective or subjective, regarding any event in question. We argue that the formation of such beliefs calls for a deeper examination and for explicit modeling. Models of belief formation may enhance our understanding of the probabilistic beliefs when these exist, and may also help us characterize situations in which entertaining such beliefs is neither realistic nor necessarily rational.

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48

Timotić, Doroteja, and Feđa Netjasov. "Modelling of runway excursions using Bayesian belief networks." Tehnika 74, no. 1 (2019): 105–12. http://dx.doi.org/10.5937/tehnika1901105t.

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49

Ardiansyah, Darfian, Wildan Suharso, and Gita Indah Marthasari. "Analisis Penerima Bantuan Sosial menggunakan Bayesian Belief Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 2, no. 2 (June 23, 2018): 506–13. http://dx.doi.org/10.29207/resti.v2i2.447.

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Bantuan Sosial merupakan pengeluaran berupa uang, barang, atau jasa yang diberikan oleh pemerintah pusat atau daerah kepada masyarakat guna melindungi masyarakat dari kemungkinan terjadinya risiko sosial, meningkatkan kemampuan ekonomi, serta kesejahteraan masyarakat. Pada kesejahteraan masyarakat, terdapat masalah yang masih belum terselesaikan sampai saat ini, yaitu kemiskinan. Kemiskinan merupakan masalah sosial yang masih belum terselesaikan di negara-negara berkembang termasuk diantaranya adalah Indonesia. Pada kemiskinan sendiri, seseorang dinyatakan miskin apabila pendapatanya lebih rendah dari garis kemiskinan serta tidak bisa untuk memenuhi kebutuhan sehari-hari. Dari permasalahan tersebut maka diperlukan analisis lebih lanjut untuk mencari kriteria yang paling berpengaruh yang nantinya dapat digunakan untuk memaksimalkan program yang telah dibuat agar taraf ekonomi pada rumah tangga sasaran dapat bertambah. Penelitian ini dilakukan untuk menganalisis faktor yang mempengaruhi perubahan taraf ekonomi menggunakan metode Bayesian Belief Network. Dari skenario yang telah dilakukan pada pengujian menggunakan metode Bayesian Belief Network ditemukan bahwa peningkatan pada kesejahteraan dan tabungan pada masyarakat di desa Srigading mampu meningkatkan perubahan taraf ekonomi hingga 71%.

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Palonen, Vesa. "A Bayesian Baseline for Belief in Uncommon Events." European Journal for Philosophy of Religion 9, no. 3 (September 21, 2017): 159–75. http://dx.doi.org/10.24204/ejpr.v9i3.1909.

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Abstract:

The plausibility of uncommon events and miracles based on testimony of such an event has been much discussed. When analyzing the probabilities involved, it has mostly been assumed that the common events can be taken as data in the calculations. However, we usually have only testimonies for the common events. While this difference does not have a significant effect on the inductive part of the inference, it has a large influence on how one should view the reliability of testimonies. In this work, a full Bayesian solution is given for the more realistic case, where one has a large number of testimonies for a common event and one testimony for an uncommon event. A free-running parameter is given for the unreliability of testimony, to be determined from data. It is seen that, in order for there to be a large amount of testimonies for a common event, the testimonies will probably be quite reliable. For this reason, because the testimonies are quite reliable based on the testimonies for the common events, the probability for the uncommon event, given a testimony for it, is also higher. Perhaps surprisingly, in the simple case, the increase in plausibility from testimony for the uncommon events is of the same magnitude as the decrease in plausibility from induction. In summary, one should be more open-minded when considering the plausibility of uncommon events.

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