Bayesian Inference

Charles A. Rohde
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Charles A. Rohde: Johns Hopkins University, Bloomberg School of Health

Chapter Chapter 14 in Introductory Statistical Inference with the Likelihood Function, 2014, pp 167-180 from Springer

Abstract: Abstract In the frequentist approach to parametric statistical inference: 1. Probability models are based on the relative frequency interpretation of probabilities. 2. Parameters of the resulting probability models are assumed to be fixed, unknown constants. 3. Observations on random variables with a probability model depending on the parameters are used to construct statistics. These are used to make inferential statements about the parameters. 4. Inferences are evaluated and interpreted on the basis of the sampling distribution of the statistics used for the inference. Thus an interval which claims to be a 95 % confidence interval for θ has the property that it contains θ 95 % of the time in repeated use. 5. In all cases inferences are evaluated on the basis of data not observed.

Keywords: Maximum Likelihood Estimate; Posterior Distribution; Prior Distribution; Beta Distribution; Fisher Information Matrix (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-10461-4_14

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DOI: 10.1007/978-3-319-10461-4_14

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