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# 1 - 3 of 3 results for: STATS300A

## STATS 270:Bayesian Statistics I (STATS 370)

This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of Stats300A, Stats305, and Stats310.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Wong, W. (PI)

## STATS 300A:Theory of Statistics I

Finite sample optimality of statistical procedures; Decision theory: loss, risk, admissibility; Principles of data reduction: sufficiency, ancillarity, completeness; Statistical models: exponential families, group families, nonparametric families; Point estimation: optimal unbiased and equivariant estimation, Bayes estimation, minimax estimation; Hypothesis testing and confidence intervals: uniformly most powerful tests, uniformly most accurate confidence intervals, optimal unbiased and invariant tests. Prerequisites: Real analysis, introductory probability (at the level of STATS 116), and introductory statistics.
Terms: Aut | Units: 2-3 | Grading: Letter or Credit/No Credit

## STATS 370:Bayesian Statistics I (STATS 270)

This is the first of a two course sequence on modern Bayesian statistics. Topics covered include: real world examples of large scale Bayesian analysis; basic tools (models, conjugate priors and their mixtures); Bayesian estimates, tests and credible intervals; foundations (axioms, exchangeability, likelihood principle); Bayesian computations (Gibbs sampler, data augmentation, etc.); prior specification. Prerequisites: statistics and probability at the level of Stats300A, Stats305, and Stats310.
Terms: Win | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Wong, W. (PI)
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