Mike’s research and teaching activities are in Bayesian analysis in ranges of interlinked areas: theory and methods of dynamic models in time series analysis, multivariate analysis, latent structure, high-dimensional inference and computation, quantitative and computational decision analysis, stochastic computational methods, and statistical computing, among other topics.
Interdisciplinary R&D has ranged across applications in signal processing, finance, econometrics, climatology, systems biology, genomics and neuroscience, among other areas. While past activity in specific research areas always indicates a long-term interest, some of Mike’s most active current and likely near-term areas of interdisciplinary collaboration and application are in macroeconomic forecasting and policy decisions, financial econometric forecasting and decisions, dynamic network studies in IT/commerce, and large-scale forecasting and decision problems in business and industry.
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