Browsing by Subject "Sequential Monte Carlo"
Now showing items 1-6 of 6
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Advances in Bayesian Modelling and Computation: Spatio-Temporal Processes, Model Assessment and Adaptive MCMC
(2009)The modelling and analysis of complex stochastic systems with increasingly large data sets, state-spaces and parameters provides major stimulus to research in Bayesian nonparametric methods and Bayesian computation. This ... -
Approximate Bayesian Computation for Complex Dynamic Systems
(2013)This thesis focuses on the development of ABC methods for statistical modeling in complex dynamic systems. Motivated by real applications in biology, I propose computational strategies for Bayesian inference in contexts ... -
Bayesian Analysis and Computational Methods for Dynamic Modeling
(2009)Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient ... -
Bayesian Emulation for Sequential Modeling, Inference and Decision Analysis
(2016)The advances in three related areas of state-space modeling, sequential Bayesian learning, and decision analysis are addressed, with the statistical challenges of scalability and associated dynamic sparsity. The key theme ... -
Finite Sample Bounds and Path Selection for Sequential Monte Carlo
(2018)Sequential Monte Carlo (SMC) samplers have received attention as an alternative to Markov chain Monte Carlo for Bayesian inference problems due to their strong empirical performance on difficult multimodal problems, natural ... -
Structural Estimation Using Sequential Monte Carlo Methods
(2011)This dissertation aims to introduce a new sequential Monte Carlo (SMC) based estimation framework for structural models used in macroeconomics and industrial organization. Current Markov chain Monte Carlo (MCMC) estimation ...
