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Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making

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Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops (AIAI 2022)

Abstract

Monte Carlo simulations using Markov chains as the Gibbs sampler and Metropolis algorithm are widely used techniques for modelling stochastic problems for decision making. Like all other Monte Carlo approaches, MCMC exploits the law of large numbers via repeated random sampling. Samples are formed by running a Markov Chain that is constructed in such a way that its stationary distribution closely matches the input function, which is represented by a proposal distribution. In this paper, the fundamentals of MCMC methods are discussed, including the algorithm selection process, optimizations, as well as some efficient approaches for utilizing generalized linear mixed models. Another aim of this paper is to highlight the usage of the EM method to get accurate maximum likelihood estimates in the context of generalized linear mixed models.

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Acknowledgements

This paper is funded in the framework of THLEMAXOS project which is funded by the Ionian Region Islands with MIS code 5007986 in the context of Operational Program Ionian Islands 2014-2020.

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Correspondence to Christos Karras .

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Karras, C., Karras, A., Avlonitis, M., Giannoukou, I., Sioutas, S. (2022). Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 652. Springer, Cham. https://doi.org/10.1007/978-3-031-08341-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-08341-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08340-2

  • Online ISBN: 978-3-031-08341-9

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