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General Information
    • ISSN: 2382-6185
    • Abbreviated Title: Int. J. Knowl. Eng.
    • Frequency: Semiyearly
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2016 Vol.2(2): 92-95 ISSN: 2382-6185
doi: 10.18178/ijke.2016.2.2.059

Visual Materials to Teach Gibbs Sampler

Yukari Shirota, Takako Hashimoto, and Basabi Chakraborty
Abstract—Bayesian model of inference is widely used in various application fields such as data engineering or text processing. Using Bayes’ theorem, we can obtain the posterior distribution function. When we conduct sampling using Markov chain Monte Carlo (MCMC), the most prominent MCMC algorithms are the Metropolis-Hastings and the Gibbs sampler, the latter being particularly useful in Bayesian analysis. This paper presents the visual teaching material for studying Gibbs sampler algorithm. Interaction with this material is supposed to enable students to deeply understand the mathematical process behind Gibbs sampling and encourages them to comprehend the mathematical expressions in the textbooks.

Index Terms—Bayesian inference, MCMC, simple topic model, Gibbs sampler, visualization.

Y. Shirota is with Faculty of Economics, GakushuinUniversityy, 1-5-1 Mejiro, Toshima-ku Tokyo, 171-8588 Japan (e-mail: yukari.shirota-atmark-gakushuin.ac.jp).
T. Hashimoto is with Chiba University of Commerce, 1-3-1, Konodai Ichikawa City, Chiba, 272-8512, Japan (e-mail: takako-atmark-cuc.ac.jp).
B. Chakraborty is with the Department of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate 020-0693, Japan (e-mail: basabi-atmark-iwate-pu.ac.jp).


Cite: Yukari Shirota, Takako Hashimoto, and Basabi Chakraborty, "Visual Materials to Teach Gibbs Sampler," International Journal of Knowledge Engineering vol. 2, no. 2, pp. 92-95, 2016.

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