Accelerating Monte Carlo methods for Bayesian inference in dynamical models (2016)
Abstract / truncated to 115 words
Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often ... toggle 11 keywordscomputational statistics – monte carlo – markov chains – particle filters – machine learning – bayesian optimisation – approximate bayesian computations – gaussian processes – particle metropolis-hastings – approximate inference – pseudo-marginal methods
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