Relevant bibliographies by topics / Gibbs sampler

Academic literature on the topic 'Gibbs sampler'

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Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Gibbs sampler"

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Ritter, Christian, and Martin A. Tanner. "Facilitating the Gibbs Sampler: The Gibbs Stopper and the Griddy-Gibbs Sampler." Journal of the American Statistical Association 87, no. 419 (September 1992): 861–68. http://dx.doi.org/10.1080/01621459.1992.10475289.

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Shao, Wei, Guo Qing Zhao, and Yu Jie Gai. "Mixture Normal Distribution for Gibbs Sampler and its Application in the Surface of Single Crystal." Advanced Materials Research 529 (June 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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MacEachern, Steven N., and L. Mark Berliner. "Subsampling the Gibbs Sampler." American Statistician 48, no. 3 (August 1994): 188. http://dx.doi.org/10.2307/2684714.

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Casella, George, and Edward I. George. "Explaining the Gibbs Sampler." American Statistician 46, no. 3 (August 1992): 167. http://dx.doi.org/10.2307/2685208.

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Thompson, W. A., L. A. Newberg, S. Conlan, L. A. McCue, and C. E. Lawrence. "The Gibbs Centroid Sampler." Nucleic Acids Research 35, Web Server (May 8, 2007): W232—W237. http://dx.doi.org/10.1093/nar/gkm265.

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Casella, George, and Edward I. George. "Explaining the Gibbs Sampler." American Statistician 46, no. 3 (August 1992): 167–74. http://dx.doi.org/10.1080/00031305.1992.10475878.

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Maceachern, Steven N., and L. Mark Berliner. "Subsampling the Gibbs Sampler." American Statistician 48, no. 3 (August 1994): 188–90. http://dx.doi.org/10.1080/00031305.1994.10476054.

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Zellner, Arnold, and Chung-Ki Min. "Gibbs Sampler Convergence Criteria." Journal of the American Statistical Association 90, no. 431 (September 1995): 921–27. http://dx.doi.org/10.1080/01621459.1995.10476591.

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Utsugi, Akio, and Toru Kumagai. "Bayesian Analysis of Mixtures of Factor Analyzers." Neural Computation 13, no. 5 (May 1, 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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Samawi, Hani M., Martin Dunbar, and Ding-Geng (Din) Chen. "Steady-state ranked Gibbs sampler." Journal of Statistical Computation and Simulation 82, no. 8 (August 2012): 1223–38. http://dx.doi.org/10.1080/00949655.2011.575378.

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Dissertations / Theses on the topic "Gibbs sampler"

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Chimisov, Cyril. "Adapting the Gibbs sampler." Thesis, University of Warwick, 2018. http://wrap.warwick.ac.uk/108829/.

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In the present thesis, we close a methodological gap of optimising the basic Markov Chain Monte Carlo algorithms. Similarly to the straightforward and computationally efficient optimisation criteria for the Metropolis algorithm acceptance rate (and, equivalently, proposal scale), we develop criteria for optimising the selection probabilities of the Random Scan Gibbs Sampler. We develop a general purpose Adaptive Random Scan Gibbs Sampler, that adapts the selection probabilities, gradually, as further information is accrued by the sampler. We argue that Adaptive Random Scan Gibbs Samplers can be routinely implemented and substantial computational gains will be observed across many typical Gibbs sampling problems. Additionally, motivated to develop theory to analyse convergence properties of the Adaptive Gibbs Sampler, we introduce a class of Adapted Increasingly Rarely Markov Chain Monte Carlo (AirMCMC) algorithms, where the underlying Markov kernel is allowed to be changed based on the whole available chain output, but only at specific time points separated by an increasing number of iterations. The main motivation is the ease of analysis of such algorithms. Under regularity assumptions, we prove the Mean Square Error convergence, Weak and Strong Laws of Large Numbers, and the Central Limit Theorem and discuss how our approach extends the existing results. We argue that many of the known Adaptive MCMC algorithms may be transformed into the corresponding Air versions and provide an empirical evidence that performance of the Air version remains virtually the same.
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Pang, Wan-Kai. "Modelling ordinal categorical data : a Gibbs sampler approach." Thesis, University of Southampton, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.323876.

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Fair, Shannon Marie. "A Bayesian Meta-Analysis Using the Gibbs Sampler." UNF Digital Commons, 1998. http://digitalcommons.unf.edu/etd/87.

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A meta-analysis is the combination of results from several similar studies, conducted by different scientists, in order to arrive at a single, overall conclusion. Unlike common experimental procedures, the data used in a meta-analysis happen to be the descriptive statistics from the distinct individual studies. In this thesis, we will consider two regression studies performed by two scientists. These studies have one common dependent variable, Y, and one or more independent common variables, X. A regression of Y on X with other independent variables is carried out on both studies. We will estimate the regression coefficients of X meta-analytically. After combining the two studies, we will derive a single regression model. There will be observations that one scientist witnesses and the other does not. The missing observations are considered parameters and are estimated using a method called Gibbs sampling.
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Zhang, Zuoshun. "Proper posterior distributions for some hierarchical models and roundoff effects in the Gibbs sampler /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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Tan, Aixin. "Convergence rates and regeneration of the block Gibbs sampler for Bayesian random effects models." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024910.

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Al-Hamzawi, Rahim Jabbar Thaher. "Prior elicitation and variable selection for bayesian quantile regression." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7501.

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Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression coefficients vector given a specific model and Bayesian computational efficiency (Chen et al., 1999). These difficulties become more challenging in Bayesian quantile regression framework when one is interested in assigning priors that depend on different quantile levels. The objective of Bayesian quantile regression (BQR), which is a newly proposed tool, is to deal with unknown parameters and model uncertainty in quantile regression (QR). However, Bayesian subset selection in quantile regression models is usually a difficult issue due to the computational challenges and nonavailability of conjugate prior distributions that are dependent on the quantile level. These challenges are rarely addressed via either penalised likelihood function or stochastic search variable selection (SSVS). These methods typically use symmetric prior distributions for regression coefficients, such as the Gaussian and Laplace, which may be suitable for median regression. However, an extreme quantile regression should have different regression coefficients from the median regression, and thus the priors for quantile regression coefficients should depend on quantiles. This thesis focuses on three challenges: assigning standard quantile dependent prior distributions for the regression coefficients, assigning suitable quantile dependent priors over model space and achieving computational efficiency. The first of these challenges is studied in Chapter 2 in which a quantile dependent prior elicitation scheme is developed. In particular, an extension of the Zellners prior which allows for a conditional conjugate prior and quantile dependent prior on Bayesian quantile regression is proposed. The prior is generalised in Chapter 3 by introducing a ridge parameter to address important challenges that may arise in some applications, such as multicollinearity and overfitting problems. The proposed prior is also used in Chapter 4 for subset selection of the fixed and random coefficients in a linear mixedeffects QR model. In Chapter 5 we specify normal-exponential prior distributions for the regression coefficients which can provide adaptive shrinkage and represent an alternative model to the Bayesian Lasso quantile regression model. For the second challenge, we assign a quantile dependent prior over model space in Chapter 2. The prior is based on the percentage bend correlation which depends on the quantile level. This prior is novel and is used in Bayesian regression for the first time. For the third challenge of computational efficiency, Gibbs samplers are derived and setup to facilitate the computation of the proposed methods. In addition to the three major aforementioned challenges this thesis also addresses other important issues such as the regularisation in quantile regression and selecting both random and fixed effects in mixed quantile regression models.
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Yankovskyy, Yevhen. "Application of a Gibbs Sampler to estimating parameters of a hierarchical normal model with a time trend and testing for existence of the global warming." Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1010.

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Xu, Zhiqing. "Bayesian Inference of a Finite Population under Selection Bias." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/621.

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Length-biased sampling method gives the samples from a weighted distribution. With the underlying distribution of the population, one can estimate the attributes of the population by converting the weighted samples. In this thesis, generalized gamma distribution is considered as the underlying distribution of the population and the inference of the weighted distribution is made. Both the models with known and unknown finite population size are considered. In the modes with known finite population size, maximum likelihood estimation and bootstrapping methods are attempted to derive the distributions of the parameters and population mean. For the sake of comparison, both the models with and without the selection bias are built. The computer simulation results show the model with selection bias gives better prediction for the population mean. In the model with unknown finite population size, the distributions of the population size as well as the sample complements are derived. Bayesian analysis is performed using numerical methods. Both the Gibbs sampler and random sampling method are employed to generate the parameters from their joint posterior distribution. The fitness of the size-biased samples are checked by utilizing conditional predictive ordinate.
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Plassmann, Florenz. "The Impact of Two-Rate Taxes on Construction in Pennsylvania." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30622.

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The evaluation of policy-relevant economic research requires an ethical foundation. Classical liberal theory provides the requisite foundation for this dissertation, which uses various econometric tools to estimate the effects of shifting some of the property tax from buildings to land in 15 cities in Pennsylvania. Economic theory predicts that such a shift will lead to higher building activity. However, this prediction has been supported little by empirical evidence so far.

The first part of the dissertation examines the effect of the land-building tax differential on the number of building permits that were issued in 219 municipalities in Pennsylvania between 1972 and 1994. For such count data a conventional analysis based on a continuous distribution leads to incorrect results; a discrete maximum likelihood analysis with a negative binomial distribution is more appropriate. Two models, a non-linear and a fixed effects model, are developed to examine the influence of the tax differential. Both models suggest that this influence is positive, albeit not statistically significant.

Application of maximum likelihood techniques is computationally cumbersome if the assumed distribution of the data cannot be written in closed form. The negative binomial distribution is the only discrete distribution with a variance that is larger than its mean that can easily be applied, although it might not be the best approximation of the true distribution of the data. The second part of the dissertation uses a Markov Chain Monte Carlo method to examine the influence of the tax differential on the number of building permits, under the assumption that building permits are generated by a Poisson process whose parameter varies lognormally. Contrary to the analysis in the first part, the tax is shown to have a strong and significantly positive impact on the number of permits.

The third part of the dissertation uses a fixed-effects weighted least squares method to estimate the effect of the tax differential on the value per building permit. The tax coefficient is not significantly different from zero. Still, the overall impact of the tax differential on the total value of construction is shown to be positive and statistically significant.


Ph. D.

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Cao, Jun. "A Random-Linear-Extension Test Based on Classic Nonparametric Procedures." Diss., Temple University Libraries, 2009. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/48271.

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Statistics
Ph.D.
Most distribution free nonparametric methods depend on the ranks or orderings of the individual observations. This dissertation develops methods for the situation when there is only partial information about the ranks available. A random-linear-extension exact test and an empirical version of the random-linear-extension test are proposed as a new way to compare groups of data with partial orders. The basic computation procedure is to generate all possible permutations constrained by the known partial order using a randomization method similar in nature to multiple imputation. This random-linear-extension test can be simply implemented using a Gibbs Sampler to generate a random sample of complete orderings. Given a complete ordering, standard nonparametric methods, such as the Wilcoxon rank-sum test, can be applied, and the corresponding test statistics and rejection regions can be calculated. As a direct result of our new method, a single p-value is replaced by a distribution of p-values. This is related to some recent work on Fuzzy P-values, which was introduced by Geyer and Meeden in Statistical Science in 2005. A special case is to compare two groups when only two objects can be compared at a time. Three matching schemes, random matching, ordered matching and reverse matching are introduced and compared between each other. The results described in this dissertation provide some surprising insights into the statistical information in partial orderings.
Temple University--Theses
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Books on the topic "Gibbs sampler"

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Kuo, Lynn. Bayesian computations in survival models via the Gibbs sampler. Monterey, Calif: Naval Postgraduate School, 1991.

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Canty, Angelo. A system to test for convergence of the Gibbs sampler. Toronto: [s.n.], 1994.

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Gibbs, Alison. Bounding convergence time of the Gibbs sampler in Bayesian image restoration. Toronto: University of Toronto, Dept. of Statistics, 1998.

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Rosenthal, Jeffrey S. Analysis of the Gibbs sampler for a model related to James-Stein estimators. [Toronto]: University of Toronto, Dept. of Statistics, 1994.

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Roberts, Gareth O. On convergence rates of Gibbs samplers for uniform distributions. [Toronto: University of Toronto, 1997.

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Canty, Angelo. A system to test for convergence of Gibbs Sampler. 1995.

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Jun, Liu. Correlation structure and convergence rate of the Gibbs sampler. 1991.

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Suess, Eric A., and Bruce E. Trumbo. Gibbs Sampling and Screening Tests: From Random Numbers to the Gibbs Sampler (Springer Texts in Statistics). Springer, 2006.

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Advances in full-information item factor analysis using the Gibbs sampler. 1993.

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Jandaghi, Gholamreza. Monte Carlo estimation of the distributions of the pedigree likelihood, the score statistic and the likelihood ratio statistic using the Gibbs Sampler. 1994.

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Book chapters on the topic "Gibbs sampler"

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Armstrong, Margaret, Alain G. Galli, Gaëlle Le Loc’h, François Geffroy, and Rémi Eschard. "Gibbs Sampler." In Plurigaussian Simulations in Geosciences, 77–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-12718-6_6.

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Armstrong, Margaret, Alain Galli, Hélène Beucher, Gaëlle Le Loc’h, Didier Renard, Brigitte Doligez, Rémi Eschard, and François Geffroy. "Gibbs Sampler." In Plurigaussian Simulations in Geosciences, 107–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19607-2_7.

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Xia, Xuhua. "Gibbs sampler." In Bioinformatics and the Cell, 99–111. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90684-3_4.

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Liu, Jun S. "The Gibbs Sampler." In Springer Series in Statistics, 129–51. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-76371-2_6.

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Robert, Christian P., and George Casella. "The Gibbs Sampler." In Springer Texts in Statistics, 285–361. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3071-5_7.

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Tanner, Martin A. "The Gibbs Sampler." In Tools for Statistical Inference, 89–107. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4684-0510-1_6.

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Robert, Christian P., and George Casella. "The Multi-Stage Gibbs Sampler." In Springer Texts in Statistics, 371–424. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4145-2_10.

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Robert, Christian P., and George Casella. "The Two-Stage Gibbs Sampler." In Springer Texts in Statistics, 337–70. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4145-2_9.

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Barbu, Adrian, and Song-Chun Zhu. "Gibbs Sampler and Its Variants." In Monte Carlo Methods, 97–121. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_5.

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Keith, Jonathan, George Sofronov, and Dirk Kroese. "The Generalized Gibbs Sampler and the Neighborhood Sampler." In Monte Carlo and Quasi-Monte Carlo Methods 2006, 537–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-74496-2_31.

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Conference papers on the topic "Gibbs sampler"

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Webb, Jon A. "Accurate halftoning using the Gibbs sampler." In EI 92, edited by James R. Sullivan, Benjamin M. Dawson, and Majid Rabbani. SPIE, 1992. http://dx.doi.org/10.1117/12.58335.

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Simandl, Miroslav, and Tomas Soukup. "Gibbs sampler to stochastic volatility models." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076061.

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Ai, Hua, Yang Lu, and Wenbin Guo. "Distributed Bayesian Compressive Sensing using Gibbs sampler." In 2012 International Conference on Wireless Communications & Signal Processing (WCSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcsp.2012.6542872.

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Lu Ling-yun, Xiao Yang, and Du Hai-feng. "Adaptive multiuser detection based on Gibbs sampler." In IET 2nd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2008). IEE, 2008. http://dx.doi.org/10.1049/cp:20080982.

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Blunsom, Phil, Trevor Cohn, Chris Dyer, and Miles Osborne. "A Gibbs sampler for phrasal synchronous grammar induction." In the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1690219.1690256.

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Orieux, F., O. Feron, and J. F. Giovannelli. "Gradient scan Gibbs sampler: An efficient high-dimensional sampler application in inverse problems." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178739.

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Walker, Daniel David, and Eric K. Ringger. "Model-based document clustering with a collapsed gibbs sampler." In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401975.

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Sari, Ilkay, Erchin Serpedin, and Bruce Suter. "Application of Gibbs Sampler for Clock Synchronization in Rbs-Protocol." In MILCOM 2006. IEEE, 2006. http://dx.doi.org/10.1109/milcom.2006.302368.

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Kail, Georg, Jean-Yves Tourneret, Franz Hlawatsch, and Nicolas Dobigeon. "A partially collapsed Gibbs sampler for parameters with local constraints." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495806.

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Jinghua Gu, Chen Wang, Le-Ming Shih, Tian-Li Wang, Yue Wang, R. Clarke, and Jianhua Xuan. "GIST: A Gibbs sampler to identify intracellular signal transduction pathways." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090677.

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Reports on the topic "Gibbs sampler"

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Raftery, Adrian E., and Steven Lewis. How Many Iterations in the Gibbs Sampler? Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada640705.

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Kuo, Lynn, and Adrian F. Smith. Bayesian Computations in Survival Models via the Gibbs Sampler. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada242343.

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Wakefield, J. C., A. F. Smith, A. Racine-Poon, and A. E. Gelfand. Bayesian Analysis of Linear and Nonlinear Population Models Using the Gibbs Sampler. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada254769.

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Raftery, Adrian E., Steven Lewis, and Jeffrey D. Banfield. Three Short Papers on Sampling-Based Inference: 1. How Many Iterations in the Gibbs Sampler? 2. Model Determination. 3. Spatial Statistics. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada241409.

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Cheng, Hao, Rohan L. Fernando, and Dorian J. Garrick. Three Different Gibbs Samplers for BayesB Genomic Prediction. Ames (Iowa): Iowa State University, January 2014. http://dx.doi.org/10.31274/ans_air-180814-1152.

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