Understanding Statistical Inference

دورة معتمدة اون لاين مجانية Statistical inferences rely on probabilities. Bayesian statistics employs probabilities that are relative to evidence. Bayesian statistics studies decisions to illuminate the probabilities it uses to analyze statistical inferences. This chapter explains the Bayesian decision-theoretic approach to statistics. A statistical test of a hypothesis collects statistical data to evaluate the hypothesis. The Bayesian decision-theoretic approach to statistics uses a statistical test along with prior information to evaluate a hypothesis. Bayesians methods generate the hypothesis's probability at the conclusion of the test. This chapter describes Bayesian probabilities' function in representations of decision problems and considers how to use the relationship between Bayesian probabilities and decisions to illuminate these probabilities. It takes them to be rational degrees of belief and does not define them in terms of preferences. The properties of Bayesian probabilities are explored and strategies for meeting objections are presented. Bayesianism advances conditionalization as a rule of probabilistic inference and maximization of expected utility as a decision rule. It compares Bayesian and classical statistics and defends Bayesian statistics against the charge of inordinate subjectivism. Dr Nic's Maths and Stats Understanding Statistical Inference