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Applied statistical modelling - Coggle Diagram
Applied statistical modelling
Naive Bayes
Conditional independence
Independent random variables
Exchangeability
The first urn process
Polya urn
De Finetti theorem
Infinite exchangeability = conditional independence
Bayesian model
Predictive approach
Hierarchical models
Conjugacy
Conjugate prior
Gamma-Poisson
Beta-Binomial
Normal-Normal
Inversegamma-Normal
Monte Carlo integration
Monte Carlo Error
Non-conjugate models
Non-conjugate priors
Markov Chains
Features
Invariant distribution
Reversibility
Autocorrelation function
Transition Kernel
Ergodic mean
Unbiased
Consistent
Irreducibility
Harris-recurrent
LLN and CLT for Markov Chains
2 types of Markov Chain
Metropolis-Hastings
Instrumental distribution proposal
Gibbs Sampler
Special case of a MH algorithm
Random sweep
Systematic sweep
Convergence analysis
Burn-in
Geweke diagnostic
Gelman and Rubin diagnostic
2 problems
Convergence of the MC to stationary distribution
Dependence between elements of the Markov Chain
Thinning