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Journal articles on the topic 'Bayesian models'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

McGlothlin, Anna E., and Kert Viele. "Bayesian Hierarchical Models." JAMA 320, no. 22 (December 11, 2018): 2365. http://dx.doi.org/10.1001/jama.2018.17977.

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2

Figini, Silvia, Lijun Gao, and Paolo Giudici. "Bayesian operational risk models." Journal of Operational Risk 10, no. 2 (June 2015): 45–60. http://dx.doi.org/10.21314/jop.2015.155.

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3

Furmańczyki, Konrad, and Wojciech Niemiro. "Sufficiency in bayesian models." Applicationes Mathematicae 25, no. 1 (1998): 113–20. http://dx.doi.org/10.4064/am-25-1-113-120.

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4

Taylor, Greg. "BAYESIAN CHAIN LADDER MODELS." ASTIN Bulletin 45, no. 1 (October 17, 2014): 75–99. http://dx.doi.org/10.1017/asb.2014.25.

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AbstractThe literature on Bayesian chain ladder models is surveyed. Both Mack and cross-classified forms of the chain ladder are considered. Both cases are examined in the context of error terms distributed according to a member of the exponential dispersion family. Tweedie and over-dispersed Poisson errors follow as special cases. Bayesian cross-classified chain ladder models may randomise row, column or diagonal parameters. Column and diagonal randomisation has been largely absent from the literature until recently. The present paper allows randomisation of row and column parameters. The Bayes estimator, the linear Bayes (credibility) estimator, and the MAP estimator are shown to be identical in the Mack case, and in the cross-classified case provided that the error terms are Tweedie distributed. In the Mack case the variance structure is generalised considerably from the existing literature. In the cross-classified case the model structure differs somewhat from the existing literature, and a comparison is made between the two. MAP estimators for the cross-classified case are often given by implicit equations that require numerical solution. Recursive formulas are given for these in the general case of error terms from the exponential dispersion family. The connection between the cross-classified case and Bornhuetter-Ferguson prediction is explored.

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5

Stamps, Judy A., and Willem E. Frankenhuis. "Bayesian Models of Development." Trends in Ecology & Evolution 31, no. 4 (April 2016): 260–68. http://dx.doi.org/10.1016/j.tree.2016.01.012.

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6

Da-Silva, Cibele Queiroz, and Guilherme Souza Rodrigues. "Bayesian Dynamic Dirichlet Models." Communications in Statistics - Simulation and Computation 44, no. 3 (September 10, 2014): 787–818. http://dx.doi.org/10.1080/03610918.2013.795592.

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7

da-Silva, C. Q., H. S. Migon, and L. T. Correia. "Dynamic Bayesian beta models." Computational Statistics & Data Analysis 55, no. 6 (June 2011): 2074–89. http://dx.doi.org/10.1016/j.csda.2010.12.011.

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8

Cochran, James J., Martin S. Levy, and Jeffrey D. Camm. "Bayesian coverage optimization models." Journal of Combinatorial Optimization 19, no. 2 (June 29, 2008): 158–73. http://dx.doi.org/10.1007/s10878-008-9172-y.

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9

Grave de Peralta Menendez, Rolando, Amal Achaïbou, Pierre Bessière, Patrik Vuilleumier, and Sara Gonzalez Andino. "Bayesian Models of Mentalizing." Brain Topography 20, no. 4 (March 20, 2008): 278–83. http://dx.doi.org/10.1007/s10548-008-0047-4.

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10

Chater, Nick, Mike Oaksford, Ulrike Hahn, and Evan Heit. "Bayesian models of cognition." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 6 (May 11, 2010): 811–23. http://dx.doi.org/10.1002/wcs.79.

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11

Hasan, Evan Abdulmajeed. "Bayesian Analysis Influences Autoregressive Models." International Journal Of Engineering, Business And Management 3, no. 3 (2019): 65–76. http://dx.doi.org/10.22161/ijebm.3.3.2.

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12

Hey, John D., Marcel Boyer, and Richard E. Kihlstrom. "Bayesian Models in Economic Theory." Economic Journal 95, no. 377 (March 1985): 224. http://dx.doi.org/10.2307/2233492.

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13

Leung, Clarence. "Bayesian Models for Phylogenetic trees." McGill Science Undergraduate Research Journal 7, no. 1 (March 31, 2012): 28–34. http://dx.doi.org/10.26443/msurj.v7i1.100.

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Introduction: inferring genetic ancestry of different species is a current challenge in phylogenetics because of the immense raw biological data to be analyzed. computational techniques are necessary in order to parse and analyze all of such data in an efficient but accurate way, with many algorithms based on statistical principles designed to provide a best estimate of a phylogenetic topology. Methods: in this study, we analyzed a class of algorithms known as Markov Chain Monte Carlo (MCMC) algorithms, which uses Bayesian statistics on a biological model, and simulates the most likely evolutionary history through continuous random sampling. we combined this method with a python-based implementation on both artificially generated and actual sets of genetic data from the UCSC genome browser. results and discussion: we observe that MCMC methods provide a strong alternative to the more computationally intense likelihood algorithms and statistically weaker parsimony algorithms. given enough time, the MCMC algorithms will generate a phylogenetic tree that eventually converges to the most probable configuration

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14

Westfall, Peter H., M. West, and J. Harrison. "Bayesian Forecasting and Dynamic Models." Technometrics 34, no. 1 (February 1992): 115. http://dx.doi.org/10.2307/1269581.

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15

Ziegel, Eric R., M. West, and J. Harrison. "Bayesian Forecasting and Dynamic Models." Technometrics 39, no. 4 (November 1997): 433. http://dx.doi.org/10.2307/1271526.

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16

Koop, Gary. "Bayesian Semi-Nonparametric Arch Models." Review of Economics and Statistics 76, no. 1 (February 1994): 176. http://dx.doi.org/10.2307/2109835.

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17

Hodges, James S., Mike West, and Jeff Harrison. "Bayesian Forecasting and Dynamic Models." Journal of the American Statistical Association 86, no. 414 (June 1991): 547. http://dx.doi.org/10.2307/2290611.

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18

Tong, So Moon. "Bayesian Models for Categorical Data." Journal of the Royal Statistical Society: Series A (Statistics in Society) 169, no. 2 (March 2006): 387. http://dx.doi.org/10.1111/j.1467-985x.2006.00414_5.x.

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19

Gray, Philip. "Bayesian estimation of financial models." Accounting and Finance 42, no. 2 (June 2002): 111–30. http://dx.doi.org/10.1111/1467-629x.00070.

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20

Durham, Garland, John Geweke, Susan Porter‐Hudak, and Fallaw Sowell. "Bayesian Inference for ARFIMA Models." Journal of Time Series Analysis 40, no. 4 (January 2, 2019): 388–410. http://dx.doi.org/10.1111/jtsa.12443.

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21

Wiesenfarth, Manuel, and Thomas Kneib. "Bayesian geoadditive sample selection models." Journal of the Royal Statistical Society: Series C (Applied Statistics) 59, no. 3 (May 2010): 381–404. http://dx.doi.org/10.1111/j.1467-9876.2009.00698.x.

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22

Jesus, J. F., R. Valentim, and F. Andrade-Oliveira. "Bayesian analysis of CCDM models." Journal of Cosmology and Astroparticle Physics 2017, no. 09 (September 20, 2017): 030. http://dx.doi.org/10.1088/1475-7516/2017/09/030.

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23

Smadi, Mahmoud M., and M. T. Alodat. "Bayesian Threshold Moving Average Models." Journal of Modern Applied Statistical Methods 10, no. 1 (May 1, 2011): 262–67. http://dx.doi.org/10.22237/jmasm/1304223720.

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24

Kennedy, Marc C., and Anthony O'Hagan. "Bayesian calibration of computer models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, no. 3 (2001): 425–64. http://dx.doi.org/10.1111/1467-9868.00294.

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25

Rutter, Carolyn M., Diana L. Miglioretti, and James E. Savarino. "Bayesian Calibration of Microsimulation Models." Journal of the American Statistical Association 104, no. 488 (December 2009): 1338–50. http://dx.doi.org/10.1198/jasa.2009.ap07466.

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26

Wood, Sally, Ori Rosen, and Robert Kohn. "Bayesian Mixtures of Autoregressive Models." Journal of Computational and Graphical Statistics 20, no. 1 (January 2011): 174–95. http://dx.doi.org/10.1198/jcgs.2010.09174.

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27

Dasgupta, Nairanjana. "Bayesian Models for Categorical Data." Technometrics 49, no. 2 (May 2007): 230–31. http://dx.doi.org/10.1198/tech.2007.s492.

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28

Wang, Shuchun, Wei Chen, and Kwok-Leung Tsui. "Bayesian Validation of Computer Models." Technometrics 51, no. 4 (November 2009): 439–51. http://dx.doi.org/10.1198/tech.2009.07011.

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29

Hecht, Martin, Katinka Hardt, Charles C. Driver, and Manuel C. Voelkle. "Bayesian continuous-time Rasch models." Psychological Methods 24, no. 4 (August 2019): 516–37. http://dx.doi.org/10.1037/met0000205.

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30

Annis, David H. "Bayesian Models for Categorical Data." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 844–45. http://dx.doi.org/10.1198/jasa.2006.s95.

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31

Wong, George Y. "Bayesian Models for Directed Graphs." Journal of the American Statistical Association 82, no. 397 (March 1987): 140–48. http://dx.doi.org/10.1080/01621459.1987.10478406.

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32

Martins, Pedro, Jo ao F. Henriques, Rui Caseiro, and Jorge Batista. "Bayesian Constrained Local Models Revisited." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 4 (April 1, 2016): 704–16. http://dx.doi.org/10.1109/tpami.2015.2462343.

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33

Paun, Silviu, Bob Carpenter, Jon Chamberlain, Dirk Hovy, Udo Kruschwitz, and Massimo Poesio. "Comparing Bayesian Models of Annotation." Transactions of the Association for Computational Linguistics 6 (December 2018): 571–85. http://dx.doi.org/10.1162/tacl_a_00040.

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The analysis of crowdsourced annotations in natural language processing is concerned with identifying (1) gold standard labels, (2) annotator accuracies and biases, and (3) item difficulties and error patterns. Traditionally, majority voting was used for 1, and coefficients of agreement for 2 and 3. Lately, model-based analysis of corpus annotations have proven better at all three tasks. But there has been relatively little work comparing them on the same datasets. This paper aims to fill this gap by analyzing six models of annotation, covering different approaches to annotator ability, item difficulty, and parameter pooling (tying) across annotators and items. We evaluate these models along four aspects: comparison to gold labels, predictive accuracy for new annotations, annotator characterization, and item difficulty, using four datasets with varying degrees of noise in the form of random (spammy) annotators. We conclude with guidelines for model selection, application, and implementation.

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34

Lee, Jasme, Jonathan Rathsam, and Alyson G. Wilson. "Bayesian statistical dose-response models." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2753. http://dx.doi.org/10.1121/1.5136530.

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35

Lucy, L. B. "Frequentist tests for Bayesian models." Astronomy & Astrophysics 588 (March 11, 2016): A19. http://dx.doi.org/10.1051/0004-6361/201527709.

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36

Liu, J., and C. Lawrence. "Bayesian inference on biopolymer models." Bioinformatics 15, no. 1 (January 1, 1999): 38–52. http://dx.doi.org/10.1093/bioinformatics/15.1.38.

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37

Powell, Georgie, Zoe Meredith, Rebecca McMillin, and Tom C. A. Freeman. "Bayesian Models of Individual Differences." Psychological Science 27, no. 12 (October 23, 2016): 1562–72. http://dx.doi.org/10.1177/0956797616665351.

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According to Bayesian models, perception and cognition depend on the optimal combination of noisy incoming evidence with prior knowledge of the world. Individual differences in perception should therefore be jointly determined by a person’s sensitivity to incoming evidence and his or her prior expectations. It has been proposed that individuals with autism have flatter prior distributions than do nonautistic individuals, which suggests that prior variance is linked to the degree of autistic traits in the general population. We tested this idea by studying how perceived speed changes during pursuit eye movement and at low contrast. We found that individual differences in these two motion phenomena were predicted by differences in thresholds and autistic traits when combined in a quantitative Bayesian model. Our findings therefore support the flatter-prior hypothesis and suggest that individual differences in prior expectations are more systematic than previously thought. In order to be revealed, however, individual differences in sensitivity must also be taken into account.

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38

An, Sungbae, and Frank Schorfheide. "Bayesian Analysis of DSGE Models." Econometric Reviews 26, no. 2-4 (April 12, 2007): 113–72. http://dx.doi.org/10.1080/07474930701220071.

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39

Berliner, L. Mark. "Bayesian Control in Mixture Models." Technometrics 29, no. 4 (November 1987): 455–60. http://dx.doi.org/10.1080/00401706.1987.10488274.

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40

Kersten, Daniel, and Alan Yuille. "Bayesian models of object perception." Current Opinion in Neurobiology 13, no. 2 (April 2003): 150–58. http://dx.doi.org/10.1016/s0959-4388(03)00042-4.

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41

Penny, W. D., J. Kilner, and F. Blankenburg. "Robust Bayesian general linear models." NeuroImage 36, no. 3 (July 2007): 661–71. http://dx.doi.org/10.1016/j.neuroimage.2007.01.058.

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42

Justel, Ana, and Daniel Peña. "Bayesian unmasking in linear models." Computational Statistics & Data Analysis 36, no. 1 (March 2001): 69–84. http://dx.doi.org/10.1016/s0167-9473(00)00033-5.

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43

Walker, Stephen G. "Bayesian inference with misspecified models." Journal of Statistical Planning and Inference 143, no. 10 (October 2013): 1621–33. http://dx.doi.org/10.1016/j.jspi.2013.05.013.

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44

Ma, Wei Ji. "Bayesian Decision Models: A Primer." Neuron 104, no. 1 (October 2019): 164–75. http://dx.doi.org/10.1016/j.neuron.2019.09.037.

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45

Plummer, Martyn. "Cuts in Bayesian graphical models." Statistics and Computing 25, no. 1 (November 27, 2014): 37–43. http://dx.doi.org/10.1007/s11222-014-9503-z.

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46

Zhuang, Lili, and Noel Cressie. "Bayesian hierarchical statistical SIRS models." Statistical Methods & Applications 23, no. 4 (November 2014): 601–46. http://dx.doi.org/10.1007/s10260-014-0280-9.

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47

Fildes, Robert. "Bayesian forecasting and dynamic models." International Journal of Forecasting 8, no. 4 (December 1992): 635–37. http://dx.doi.org/10.1016/0169-2070(92)90073-i.

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48

Bhattacharya, A., and D. B. Dunson. "Sparse Bayesian infinite factor models." Biometrika 98, no. 2 (May 24, 2011): 291–306. http://dx.doi.org/10.1093/biomet/asr013.

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49

Schmidt, Klaus D. "Bayesian models in actuarial mathematics." Mathematical Methods of Operations Research 48, no. 1 (September 1998): 117–46. http://dx.doi.org/10.1007/s001860050016.

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50

Brooks, R. J., and Lyle D. Broemeling. "Bayesian Analysis of Linear Models." Applied Statistics 35, no. 1 (1986): 76. http://dx.doi.org/10.2307/2347869.

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