Advanced Monte Carlo Methods for Inference in Complex Dynamic Models

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Many physical phenomena and much data can be accurately modelled using statistical or proba-bilistic models. Examples include the volatility of the stock market, gene expressions, radar signals,relational data or images. However, even if it is possible to obtain realistic physical models or satisfactory statistical models, only the simplest can be solved without the use of numerical methods.Examples of the need for such numerical methods include non-linear non-Gaussian time series models,Markov random fields, social networks models and so on. Thanks to the advent of enormous,cheap computational power and the development of a plethora of complex inference mechanisms, itis now possible, and in many real world systems it is becoming increasingly common, to employsophisticated simulation-based techniques to provide solutions to problems previously deemed insoluble. The intention behind the research program discussed herein is to extend current, and devisenovel, simulation-based architectures to attack and efficiently solve problems that are still deemedintractable.