Efficient Bayesian analysis for complex models

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Fitting complex models with a limited number of observations is becoming increasingly necessary in various scientific and practical applications. For example, medical science faces challenges in developing disease predictive models using genetic screening tools, and economists must build a model for forecasting financial and economic indices. Traditional statistical methods fail to achieve required results due to the large number of variables involved. Standard methods must select a subset of data, ignoring the vast majority of variables, in order to manage information and achieve results. My research focuses on developing well-calibrated and computationally efficient statistical methodologies for such data sets. In my previous work, I have demonstrated mechanisms to manage large data sets (e.g. with thousands of variables) and constructed efficient software tools to implement Bayesian models to analyze such data sets. My current research will be directed towards developing more efficient Bayesian statistical methodologies and attempting to apply them to a wide variety of real problems.