Bayesian Inference in Political Science, Finance, and Marketing Research
Many current research challenges in Bayesian analysis arise in applications. A beauty of the Bayesian approach is that it facilitates principled inference in essentially any well-specified probability model or decision problem. In principle one could consider arbitrarily complicated priors, probability models and decision problems. However, not even the most creatively convoluted mind could dream up the complexities, wrinkles and complications that arise in actual applications. In this chapter we discuss typical examples of such challenges, ranging from prior constructions in political science applications, to model based data transformation for the display of multivariate marketing data, to challenging posterior simulation for state space models in finance and to expected utility maximization for portfolio selection.
KeywordsPosterior Distribution Markov Chain Monte Carlo Prior Distribution Bayesian Inference Option Price
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