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Artykuły w czasopismach na temat „Gibbs sampler”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 stycznia 2023

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1

Ritter, Christian, i Martin A. Tanner. "Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler". Journal of the American Statistical Association 87, nr 419 (wrzesień 1992): 861–68. http://dx.doi.org/10.1080/01621459.1992.10475289.

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2

Shao, Wei, Guo Qing Zhao i Yu Jie Gai. "Mixture Normal Distribution for Gibbs Sampler and its Application in the Surface of Single Crystal". Advanced Materials Research 529 (czerwiec 2012): 585–89. http://dx.doi.org/10.4028/www.scientific.net/amr.529.585.

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Gibbs sampler is widely used in Bayesian analysis. But it is often difficult to sample from the full conditional distribution, and this hardly weakens the efficiency of Gibbs sampler. In this paper, we propose to use mixture normal distribution for Gibbs sampler. The mixture normal distribution can approximate the target distribution. So carrying more information from target distribution, the mixture normal distribution tremendously improves the efficiency of Gibbs sampler. Further more, combining with mixture normal method, Hit-and-Run algorithm can also get more efficient sampling results. Simulation results show that Gibbs sampler with mixture normal distribution outperforms other sampling algorithms. The Gibbs sampler with mixture normal distribution can also be applied to explorer the surface of single crystal.

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3

MacEachern, Steven N., i L. Mark Berliner. "Subsampling the Gibbs Sampler". American Statistician 48, nr 3 (sierpień 1994): 188. http://dx.doi.org/10.2307/2684714.

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4

Casella, George, i Edward I. George. "Explaining the Gibbs Sampler". American Statistician 46, nr 3 (sierpień 1992): 167. http://dx.doi.org/10.2307/2685208.

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5

Thompson, W. A., L. A. Newberg, S. Conlan, L. A. McCue i C. E. Lawrence. "The Gibbs Centroid Sampler". Nucleic Acids Research 35, Web Server (8.05.2007): W232—W237. http://dx.doi.org/10.1093/nar/gkm265.

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6

Casella, George, i Edward I. George. "Explaining the Gibbs Sampler". American Statistician 46, nr 3 (sierpień 1992): 167–74. http://dx.doi.org/10.1080/00031305.1992.10475878.

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7

Maceachern, Steven N., i L. Mark Berliner. "Subsampling the Gibbs Sampler". American Statistician 48, nr 3 (sierpień 1994): 188–90. http://dx.doi.org/10.1080/00031305.1994.10476054.

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8

Zellner, Arnold, i Chung-Ki Min. "Gibbs Sampler Convergence Criteria". Journal of the American Statistical Association 90, nr 431 (wrzesień 1995): 921–27. http://dx.doi.org/10.1080/01621459.1995.10476591.

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9

Utsugi, Akio, i Toru Kumagai. "Bayesian Analysis of Mixtures of Factor Analyzers". Neural Computation 13, nr 5 (1.05.2001): 993–1002. http://dx.doi.org/10.1162/08997660151134299.

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For Bayesian inference on the mixture of factor analyzers, natural conjugate priors on the parameters are introduced, and then a Gibbs sampler that generates parameter samples following the posterior is constructed. In addition, a deterministic estimation algorithm is derived by taking modes instead of samples from the conditional posteriors used in the Gibbs sampler. This is regarded as a maximum a posteriori estimation algorithm with hyperparameter search. The behaviors of the Gibbs sampler and the deterministic algorithm are compared on a simulation experiment.

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10

Samawi, Hani M., Martin Dunbar i Ding-Geng (Din) Chen. "Steady-state ranked Gibbs sampler". Journal of Statistical Computation and Simulation 82, nr 8 (sierpień 2012): 1223–38. http://dx.doi.org/10.1080/00949655.2011.575378.

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11

Depaoli, Sarah, James P. Clifton i Patrice R. Cobb. "Just Another Gibbs Sampler (JAGS)". Journal of Educational and Behavioral Statistics 41, nr 6 (24.09.2016): 628–49. http://dx.doi.org/10.3102/1076998616664876.

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A review of the software Just Another Gibbs Sampler (JAGS) is provided. We cover aspects related to history and development and the elements a user needs to know to get started with the program, including (a) definition of the data, (b) definition of the model, (c) compilation of the model, and (d) initialization of the model. An example using a latent class model with large-scale education data is provided to illustrate how easily JAGS can be implemented in R. We also cover details surrounding the many programs implementing JAGS. We conclude with a discussion of the newest features and upcoming developments. JAGS is constantly evolving and is developing into a flexible, user-friendly program with many benefits for Bayesian inference.

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12

Wu, Changye, i Christian P. Robert. "Coordinate sampler: a non-reversible Gibbs-like MCMC sampler". Statistics and Computing 30, nr 3 (26.12.2019): 721–30. http://dx.doi.org/10.1007/s11222-019-09913-w.

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13

Bélisle, Claude. "Slow convergence of the Gibbs sampler". Canadian Journal of Statistics 26, nr 4 (grudzień 1998): 629–41. http://dx.doi.org/10.2307/3315722.

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14

Chan, K. S. "Asymptotic Behavior of the Gibbs Sampler". Journal of the American Statistical Association 88, nr 421 (marzec 1993): 320. http://dx.doi.org/10.2307/2290727.

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15

Shirota, Yukari, Takako Hashimoto i Basabi Chakraborty. "Visual Materials to Teach Gibbs Sampler". International Journal of Knowledge Engineering 2, nr 2 (2016): 92–95. http://dx.doi.org/10.18178/ijke.2016.2.2.059.

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16

Román, Jorge Carlos, James P. Hobert i Brett Presnell. "On reparametrization and the Gibbs sampler". Statistics & Probability Letters 91 (sierpień 2014): 110–16. http://dx.doi.org/10.1016/j.spl.2014.03.024.

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17

MacEachern, Steven N., i Mario Peruggia. "Subsampling the Gibbs sampler: variance reduction". Statistics & Probability Letters 47, nr 1 (marzec 2000): 91–98. http://dx.doi.org/10.1016/s0167-7152(99)00142-x.

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18

Chan, K. S. "Asymptotic Behavior of the Gibbs Sampler". Journal of the American Statistical Association 88, nr 421 (marzec 1993): 320–26. http://dx.doi.org/10.1080/01621459.1993.10594324.

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19

Koch, K. R. "Gibbs sampler by sampling-importance-resampling". Journal of Geodesy 81, nr 9 (23.01.2007): 581–91. http://dx.doi.org/10.1007/s00190-006-0121-1.

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20

Narisetty, Naveen N., Juan Shen i Xuming He. "Skinny Gibbs: A Consistent and Scalable Gibbs Sampler for Model Selection". Journal of the American Statistical Association 114, nr 527 (15.08.2018): 1205–17. http://dx.doi.org/10.1080/01621459.2018.1482754.

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21

Fernández, Pedro J., Pablo A. Ferrari i Sebastian P. Grynberg. "Perfectly random sampling of truncated multinormal distributions". Advances in Applied Probability 39, nr 4 (grudzień 2007): 973–90. http://dx.doi.org/10.1239/aap/1198177235.

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The target measure μ is the distribution of a random vector in a box ℬ, a Cartesian product of bounded intervals. The Gibbs sampler is a Markov chain with invariant measure μ. A ‘coupling from the past’ construction of the Gibbs sampler is used to show ergodicity of the dynamics and to perfectly simulate μ. An algorithm to sample vectors with multinormal distribution truncated to ℬ is then implemented.

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22

Fernández, Pedro J., Pablo A. Ferrari i Sebastian P. Grynberg. "Perfectly random sampling of truncated multinormal distributions". Advances in Applied Probability 39, nr 04 (grudzień 2007): 973–90. http://dx.doi.org/10.1017/s0001867800002196.

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The target measure μ is the distribution of a random vector in a box ℬ, a Cartesian product of bounded intervals. The Gibbs sampler is a Markov chain with invariant measure μ. A ‘coupling from the past’ construction of the Gibbs sampler is used to show ergodicity of the dynamics and to perfectly simulate μ. An algorithm to sample vectors with multinormal distribution truncated to ℬ is then implemented.

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23

Jovanovski, Oliver, i Neal Madras. "Convergence rates for a hierarchical Gibbs sampler". Bernoulli 23, nr 1 (luty 2017): 603–25. http://dx.doi.org/10.3150/15-bej758.

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24

Oloyede, Isiaka. "Bayesian Hetero-Elasticnet (A Gibbs Sampler Approach)". Tanzania Journal of Science 47, nr 3 (13.08.2021): 981–87. http://dx.doi.org/10.4314/tjs.v47i3.9.

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Combined heteroscedasticity and multicollinearity as dual non-spherical disturbances were experimented asymptotically. A Gibbs Sampler technique was used to investigate the asymptotic properties of hetero-elasticnet estimator with mean squares error (MSE) and bias as performance metrics. The seed was set to 12345; is set at ; Xs variables were generated as follow: the design matrix was generated from the multivariate normal distribution with mean > 0 and variance . and are truncated with Harvey (1976) heteroscedastic error structure; are collinear covariate with pairwise correlation between 0.6 and 0.9, the sample sizes were 25, 100 and 1000. The number of replications of the experiment was set at 10,000 with burn-in of 1000 which specified the draws that were discarded to remove the effects of the initial values. The thinning was set at 5 to ensure the removal of the effects of autocorrelation in the MCMC simulation. The study found that there is consistency of estimator asymptotically as the sample sizes increases from 25 to 50 so also to 1000, the larger sample size depicted least bias. The estimator exhibited efficiency asymptotically as larger sample sizes depicted least mean squares error. The study therefore recommended Bayesian hetero-elasticnet when data exhibit both heteroscedasticity and multicollinearity. Keywords: Elasticnet; Bayesian Inference and Gibbs sampler

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25

Lindsten, Fredrik, Randal Douc i Eric Moulines. "Uniform Ergodicity of the Particle Gibbs Sampler". Scandinavian Journal of Statistics 42, nr 3 (5.02.2015): 775–97. http://dx.doi.org/10.1111/sjos.12136.

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26

Walker, Stephen, i Paul Damien. "Characterizations, stochastic equations, and the Gibbs sampler". Journal of Applied Probability 36, nr 3 (wrzesień 1999): 747–51. http://dx.doi.org/10.1239/jap/1032374631.

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We obtain characterizations of densities on the real line and provide solutions to stochastic equations using the Gibbs sampler. Particular stochastic equations considered are of the type X =dB(X+C) and X =dBX+C.

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27

Waggoner, Daniel F., i Tao Zha. "A Gibbs sampler for structural vector autoregressions". Journal of Economic Dynamics and Control 28, nr 2 (listopad 2003): 349–66. http://dx.doi.org/10.1016/s0165-1889(02)00168-9.

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28

Rue, H�vard, J�rund G�semyr i Arnoldo Frigessi. "Antithetic coupling of two Gibbs sampler chains". Annals of Statistics 28, nr 4 (sierpień 2000): 1128–49. http://dx.doi.org/10.1214/aos/1015956710.

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29

Walker, Stephen, i Paul Damien. "Characterizations, stochastic equations, and the Gibbs sampler". Journal of Applied Probability 36, nr 03 (wrzesień 1999): 747–51. http://dx.doi.org/10.1017/s002190020001754x.

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We obtain characterizations of densities on the real line and provide solutions to stochastic equations using the Gibbs sampler. Particular stochastic equations considered are of the type X =d B(X+C) and X =d BX+C.

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30

Mantz, Adam B. "A Gibbs sampler for multivariate linear regression". Monthly Notices of the Royal Astronomical Society 457, nr 2 (1.02.2016): 1279–88. http://dx.doi.org/10.1093/mnras/stv3008.

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31

Alhamzawi, Rahim, i Haithem Taha Mohammad Ali. "A new Gibbs sampler for Bayesian lasso". Communications in Statistics - Simulation and Computation 49, nr 7 (15.11.2018): 1855–71. http://dx.doi.org/10.1080/03610918.2018.1508699.

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32

Smith, Aaron. "A Gibbs sampler on the $n$-simplex". Annals of Applied Probability 24, nr 1 (luty 2014): 114–30. http://dx.doi.org/10.1214/12-aap916.

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33

Kuo, Kun-Lin, i Yuchung J. Wang. "Pseudo-Gibbs sampler for discrete conditional distributions". Annals of the Institute of Statistical Mathematics 71, nr 1 (24.10.2017): 93–105. http://dx.doi.org/10.1007/s10463-017-0625-x.

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34

Wang, Neng-Yi. "Convergence rates of symmetric scan Gibbs sampler". Frontiers of Mathematics in China 14, nr 5 (28.09.2019): 941–55. http://dx.doi.org/10.1007/s11464-019-0791-7.

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35

Martino, Luca, Víctor Elvira i Gustau Camps-Valls. "The Recycling Gibbs sampler for efficient learning". Digital Signal Processing 74 (marzec 2018): 1–13. http://dx.doi.org/10.1016/j.dsp.2017.11.012.

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36

Xi, Wei, Xiaobo Tan i John S. Baras. "Gibbs sampler-based coordination of autonomous swarms". Automatica 42, nr 7 (lipiec 2006): 1107–19. http://dx.doi.org/10.1016/j.automatica.2006.03.006.

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37

Omori, Yasuhiro. "Efficient Gibbs sampler for Bayesian analysis of a sample selection model". Statistics & Probability Letters 77, nr 12 (lipiec 2007): 1300–1311. http://dx.doi.org/10.1016/j.spl.2007.03.015.

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38

Spade, David A. "Estimating drift and minorization coefficients for Gibbs sampling algorithms". Monte Carlo Methods and Applications 27, nr 3 (8.08.2021): 195–209. http://dx.doi.org/10.1515/mcma-2021-2093.

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Abstract Gibbs samplers are common Markov chain Monte Carlo (MCMC) algorithms that are used to sample from intractable probability distributions when sampling directly from full conditional distributions is possible. These types of MCMC algorithms come up frequently in many applications, and because of their popularity it is important to have a sense of how long it takes for the Gibbs sampler to become close to its stationary distribution. To this end, it is common to rely on the values of drift and minorization coefficients to bound the mixing time of the Gibbs sampler. This manuscript provides a computational method for estimating these coefficients. Herein, we detail the several advantages of the proposed methods, as well as the limitations of this approach. These limitations are primarily related to the “curse of dimensionality”, which for these methods is caused by necessary increases in the numbers of initial states from which chains need be run and the need for an exponentially increasing number of grid points for estimation of minorization coefficients.

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39

Jones, Galin L., Gareth O. Roberts i Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers". Advances in Applied Probability 46, nr 2 (czerwiec 2014): 422–45. http://dx.doi.org/10.1239/aap/1401369701.

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We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

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40

Jones, Galin L., Gareth O. Roberts i Jeffrey S. Rosenthal. "Convergence of Conditional Metropolis-Hastings Samplers". Advances in Applied Probability 46, nr 02 (czerwiec 2014): 422–45. http://dx.doi.org/10.1017/s0001867800007151.

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We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in aconditional Metropolis-Hastings sampler(CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

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41

Thompson, W. "Gibbs Recursive Sampler: finding transcription factor binding sites". Nucleic Acids Research 31, nr 13 (1.07.2003): 3580–85. http://dx.doi.org/10.1093/nar/gkg608.

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42

Keith, Jonathan M. "Segmenting Eukaryotic Genomes with the Generalized Gibbs Sampler". Journal of Computational Biology 13, nr 7 (wrzesień 2006): 1369–83. http://dx.doi.org/10.1089/cmb.2006.13.1369.

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43

Roberts, Gareth O., i Sujit K. Sahu. "Approximate Predetermined Convergence Properties of the Gibbs Sampler". Journal of Computational and Graphical Statistics 10, nr 2 (czerwiec 2001): 216–29. http://dx.doi.org/10.1198/10618600152627915.

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44

García-Cortés, LA, i D. Sorensen. "On a multivariate implementation of the Gibbs sampler". Genetics Selection Evolution 28, nr 1 (1996): 121. http://dx.doi.org/10.1186/1297-9686-28-1-121.

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45

Rajan, J. J., i A. Kawana. "Bayesian model order selection using the Gibbs sampler". Electronics Letters 32, nr 13 (1996): 1156. http://dx.doi.org/10.1049/el:19960813.

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46

LI, Kaican. "Convergence rate of Gibbs sampler and its application". Science in China Series A 48, nr 10 (2005): 1430. http://dx.doi.org/10.1360/02ys0013.

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47

Best, Nicola G., Keith K. C. Tan, Wally R. Gilks i David J. Spiegelhalter. "Estimation of population pharmacokinetics using the Gibbs sampler". Journal of Pharmacokinetics and Biopharmaceutics 23, nr 4 (sierpień 1995): 407–35. http://dx.doi.org/10.1007/bf02353641.

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48

Wills, Adrian, Thomas B. Schön, Fredrik Lindsten i Brett Ninness. "Estimation of Linear Systems using a Gibbs Sampler*". IFAC Proceedings Volumes 45, nr 16 (lipiec 2012): 203–8. http://dx.doi.org/10.3182/20120711-3-be-2027.00297.

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49

Bull, Philip, Ingunn K. Wehus, Hans Kristian Eriksen, Pedro G. Ferreira, Unni Fuskeland, Krzysztof M. Górski i Jeffrey B. Jewell. "A CMB GIBBS SAMPLER FOR LOCALIZED SECONDARY ANISOTROPIES". Astrophysical Journal Supplement Series 219, nr 1 (16.07.2015): 10. http://dx.doi.org/10.1088/0067-0049/219/1/10.

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50

Vemuri, B. C., C. Mandal i Shang-Hong Lai. "A fast Gibbs sampler for synthesizing constrained fractals". IEEE Transactions on Visualization and Computer Graphics 3, nr 4 (1997): 337–51. http://dx.doi.org/10.1109/2945.646237.

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