Metropolis-Hastings algorithm (MH) is the most popular Markov Chain Monte Carlo (MCMC) method. Essentially, the MH algorithm generates a sample, accepts or rejects the sample based on an acceptance probability that is related to the continuous target probability distribution. In this work, we propose a modified Metropolis-Hastings algorithm (MMH-DPD) that can draw samples from discrete probability distributions. For starters, the discrete probability distribution is replaced with a multimodal distribution and a new step after the rejection and acceptation step is added to the original algorithm. To reduce the error caused by the tail of the multimodal distribution, we used a mixture of Generalized Gaussians instead. Numerical results and a generalization of the proposed algorithm are provided. Our simulations show that the proposed sampler reliably creates a Markov chain that generates a sequence of values, in such a way that as the number of samples goes to infinity, we can guarantee that they reflect samples from the target discrete distribution.


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    Titel :

    Modified Metropolis-Hastings Algorithm for Efficient Sampling from Discrete Probability Distributions (MMH-DPD) Applied to Field Operational Tests database (SPMD)


    Beteiligte:


    Erscheinungsdatum :

    2018-09-01


    Format / Umfang :

    7880023 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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