Category: BayesianPage 5 of 7
Stan allows us to rapidly prototype a model without worrying about how we will fit it to data. Prophet is just the first example of scaling a statistical…
https://github.com/rmcelreath/rethinking MAP estimation Hamiltonian Monte Carlo estimation Posterior prediction Multilevel model formulas Nice covariance priors Non-centered parameterization Semi-automated Bayesian imputation Gaussian…
Ch.13 Models With Memory Example: Multilevel tadpoles Varying effects and the underfitting/overfitting trade-off More than one type of cluster Divergent transitions and non-centered priors Multilevel posterior predictions Ch.14…
Models that can simulate data: generative model Bayesian is generative
Conditional modeling Notation Formal Bayesian justification of conditional modeling Bayesian analysis of the classical regression model Notation and basic model The standard noninformative prior distribution The posterior distribution…
Part 1: Fundamentals of Bayesian Inference Part 2: Fundamentals of Bayesian Data Analysis Part 3: Advanced Computation Part 4: Regression Models Part 5: Nonlinear and Nonparametric Models …
생물통계(Biostatistics), 인과(Causality), 분류(Classification), 분할표(Contingency table), 의사 결정 이론(Decision theory), 디자인(Design), 경험적 베이즈 (Empirical Bayes), 교환가능성(Exchangeability), 유한 집단 표본추출(Finite population sampling), 일반화 선형모형(Generalized linear models), 그래프 모형(Graphical model), 계층…
13.1 finding post modes conditional max newton’s method quasi-newton and conjugate gradient methods numerical computation of derivatives 13.2 boundary-avoiding priors for modal summaries posterior modes on the boundary…
Computationally efficient Markov chain simulation 12.1 Efficient Gibbs samplers Transformations Transformations and reparameterization Auxiliary variables – modeling t dist as mix of normals 1. conditional post dist of…
Prof. Jaeyong Lee Inference 추론의 기초 Posterior Distribution 사후분포 Bayes Inference 베이즈 추론 Normal and Multi Model 정규모형, 다항모형 Monte Carlo Method 몬테카를로 방법 Gibbs Sampling 깁스샘플러 1,…