Comparing effective sampling size vs acceptance rate tuned Random Walk sampling algorithms

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dc.contributor.advisorDonselaar, Nils,
dc.contributor.advisorKwisthout, Johan
dc.contributor.authorKarakoç, Denis
dc.date.issued2020-07-10
dc.description.abstractRandom Walk Metropolis (RWM) is a stochastic approximation method to more efficiently solve Bayesian Inference problems, whose exact solutions’ computations are intractable. However, for fast convergence, a good proposal function in RWM needs to be chosen. This is no easy task, since there is no optimal set of parameters for every type of problem to be solved. To address this, several self tuning algorithms have been proposed. Traditionally, acceptance rate tuners are commonly used, which have been shown to struggle with certain types of probability distributions. We research the effectiveness of an adaptive tuning algorithm using parameters that optimize the effective sampling size and compare the algorithm with one that tunes towards an optimal average acceptance rate on those problematic target probabilities. We find that the effective sampling size tuned RWM sampler can outperform acceptance rate tuned RWM sampler for rough target densities. Results for valley targets are ambiguous and further research is required.en_US
dc.embargo.lift10000-01-01
dc.embargo.typePermanent embargoen_US
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/12677
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleComparing effective sampling size vs acceptance rate tuned Random Walk sampling algorithmsen_US
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