xenakas/bayesian_inference

Bayesian methods

Bayesian Pooling, Bayesian shrinkage, Empirical Bayes, and Stein estimation:

pymc3

The Best Of Both Worlds: Hierarchical Linear Regression in PyMC3

Why hierarchical models are awesome, tricky, and Bayesian

Examples

• Vector Autoregression (VAR) in Stan

• HVAR in Stan

• AR(1) with JAGS

• Multilevel AR(1) with JAGS

• Chineese STAN Models

Slides

• Shrinkage priors

• Bayesian Methods for Small Population Analysis

Shortreads

• Bayesian Inference

• Hierarchical Bayes and Empirical Bayes: James Stein Estimator and its EB Justification

• Bayesian Autoregressive Time Series Models

Longreads

• Bayesian shrinkage

• Hierarchical Shrinkage Priors for Regression Models

• Contraction Properties of Shrinkage Priors in Logistic Regression

• Empirical Bayes Confidence Intervals Based on Bootstrap Samples

• Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting: Kalman Filter, Conditionally Independent Hierarchical method, BVAR. Time series forecasting of demand for goods or services often involves cases subject to structural change caused by external influences like business cycles or competi- tors' actions. Methods designed for such cases, like ex- ponential smoothing (Brown 1962) and the Multi-State Kalman Filter (MSKF) method (Harrison and Stevens 1971), revise model parameter estimates over time.

• Forecasting analogous time series: Bayesian Pooling, Bayesian shrinkage, BVAR

• Forecasting international growth rates using Bayesian shrinkage and other procedures: autoregressive-leading indicator (ARLI)

• Bayesian estimation of an autoregressive model using Markov chain Monte Carlo

• Bayesian Analysis of AR (1) model

• Bayesian analysis of conditional autoregressive models

• Bayesian methods for functional and time series data: MCMC, FAR

• A Bayesian Model to Forecast New Product Performance in Domestic and International Markets

• Dynamic Bayesian Predictive Synthesis in Time Series Forecasting: Posterior Computations via MCMC, US Macroeconomic Time Series

Bayes for small samples:

• Bayesian Forecasting Methods for Short Time Series

• Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors

• Assessing Bayesian Model Comparison in Small Samples

• On Using Bayesian Methods to Address Small Sample Problems

• Contrast of Bayesian and Classical Sample Size Determination

• Forecasting Short Time Series with the Bayesian Autoregression and the Soft Computing Prior Information

BVAR

• A large Bayesian vector autoregression model for Russia

• Forecasting with Medium and Large Bayesian VARs

Numerucal Estimation

• Bayes or Bootstrap? A Simulation Study Comparing the Performance of Bayesian Markov Chain Monte Carlo Sampling and Bootstrapping

• NUMERICAL METHODS FOR ESTIMATION AND INFERENCE IN BAYESIAN VAR-MODELS