Advancements in Bayesian Methods and Implementations

1. Fisher Information, Cramer-Rao and Bayesian Paradigm
Roy Frieden
2. Compound beta binomial distribution functions
Angelo Plastino
3. MCMC for GLMMS
Vivekananda Roy
4. Signal Processing and Bayesian
Chandra Murthy
5. Mathematical theory of Bayesian statistics where all models are wrong
Sumio Watanabe
6. Machine Learning and Bayesian
Jun Zhu
7. Non-parametric Bayes
Stephen Walker
8. Bayesian testing
Christian Robert
9. Data Analysis with humans
Sumio Kaski
10. Bayesian Inference under selection
G. Alastair Young
10. Variational inference or Functional horseshoe
Anirban Bhattacharya
11. Generalized Bayes
Ryan Martin

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• Estocástica

Asymptotics; Bayesian GLMM; Bayesian inference; Bayesian model selection; Bayesian multiple testing; BIC; Covariance matching; Cross validation, Information criterion; Data augmentation; Dependent data; DIC; Dictionary learning; EM; Empirical risk minimization; False discovery rate; Free energy; Generalization loss; GLM; Hamiltonian Monte Carlo; Hypothesis testing; Improper priors; Inference approach; Information criterion; Learning rate; MALA; Markov chain Monte Carlo; Martingale; M-estimation; Metropolis-Hastings; Missing data; Mixed models; Mixtures; Model evaluation; Model misspecification; Monte Carlo; Monte Carlo maximum likelihood; Multiple comparison procedures; Noninformative prior; Particle-based variational inference; Posterior predictive; Prior evaluation; Prior specification; Probability matching; Pure states; Quantized compressed sensing; Riemannian geometry; Selective inference; Sparse Bayesian learning; spatial GLMM; Statistical learning; Stochastic gradient descent; Structured sparsity; Variable selection; WAIC; Wave functions; Wireless channel estimation