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index.Rmd
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index.Rmd
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---
title: "Home"
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
## Inference
### Likelihood Ratio and Likelihood
* [Likelihood Ratio for fully specified models, discrete data](likelihood_ratio_simple_models.html)
* [Likelihood Ratio for fully specified models, continuous data](likelihood_ratio_simple_continuous_data.html)
* Likelihood Ratios: Examples and Pitfalls
- [Examples and Pitfalls](likelihood_do_dont.html)
- [Example: LRs must be computed on same data](LR_error.html)
* [Likelihood Ratios: how big is convincing?](LR_and_BF.html)
* [Likelihood Ratios: Wilks's Theorem](wilks.html)
* [The Likelihood Function](likelihood_function.html)
* [Asymptotic Normality of MLE](asymptotic_normality_mle.html)
### Bayesian Inference
* [Bayesian inference for comparing two fully-specified models](LR_and_BF.html)
* [Bayesian inference for comparing multiple fully-specified models](bayes_multiclass.html)
* [Bayesian inference for a continuous parameter: analytic calculation](bayes_beta_binomial.html)
* [Bayesian inference for a continuous parameter: summarizing the posterior](summarize_intepret_posterior.html)
* [Bayesian inference for normal mean (known variance)](shiny_normal_example.html)
* [Relationship between Bayes Factor and p value](BF_and_pvalue.html)
### Decision Theory
* [Making a decision under uncertainty: the two class problem](decisions_costs_intro.html)
### Mixture Models
* [Introduction to Mixture Models](intro_to_mixture_models.html)
* [Introduction to EM: Gaussian Mixture Models](intro_to_em.html)
### Bayesian Computation
* [Every Bayesian computation is an integral (or a sum)](integral.html)
* [Importance sampling](Importance_sampling.html)
### Markov Chain Monte Carlo
* [Introduction to Gibbs Sampling](gibbs1.html)
* [Gibbs Sampling for a simple normal mixture](gibbs2.html)
* [Simple examples of Metropolis--Hastings algorithm](MH-examples1.html)
## Distributions
* [The Beta Distribution](beta.hmtl)
## Stochastic Processes
### Discrete-Time Markov Chains
* [Discrete-Time Markov Chains: Introduction](markov_chains_discrete_intro.html)
* [Discrete-Time Markov Chains: Finding the Stationary Distribution via solution of the global balance equations](stationary_distribution.html)
* [Discrete-Time Markov Chains: Finding the Stationary Distribution via eigendecomposition](markov_chains_discrete_stationary_dist.html)
* [Simulating Discrete-Time Markov Chains: An Introduction](simulating_discrete_chains_1.html)
* [Simulating Discrete-Time Markov Chains: Limiting Distributions](simulating_discrete_chains_2.html)
### Poisson Processes
* [Poisson Processes: Thinning with time-dependent probabilities](poisson_process_time_dependent_thinning.html)
* [Poisson Process: The Limiting Case of the Bernoulli Process](bernoulli_poisson_process.html)
### Methods for sampling non-standard distributions
* [Inverse Transform Sampling](inverse_transform_sampling.html)
### Multivariate Normal
* [Multivariate Normal](mvnorm.html)
* [Normal Markov Chain](normal_markov_chain.html)
## Population Genetics
* [Wright-Fisher Model](wright_fisher_model.html)
* [Wright-Fisher Model Approximations](approx_wright_fisher_model.html)