Contents
How to implement a Bayesian linear regression in R?
Today we are going to implement a Bayesian linear regression in R from scratch and use it to forecast US GDP growth. This post is based on a very informative manual from the Bank of England on Applied Bayesian Econometrics.
How can you make predictions with a Bayesian network?
In order to make predictions with a Bayesian network, we need to build a model. A model can be learned from data, built manually or a mixture of both. Bayesian networks are graph structures (Directed acyclic graphs, or DAGS). There is therefore no fixed structure of a network required to make predictions. Any network can make predictions.
What are the random effects in a Bayesian mixed model?
In Bayesian linear mixed models, the random effects are estimated parameters, just like the fixed effects (and thus are not BLUPs). The benefit to this is that getting interval estimates for them, or predictions using them, is as easy as anything else.
Where can I find the root of Bayesian magic?
The root of Bayesian magic is found in Bayes’ Theorem, describing the conditional probability of an event. This article is not a theoretical explanation of Bayesian statistics, but rather a step-by-step guide to building your first Bayesian model in R.
Why do we use a Bayesian approach to time series?
Here is a video going through the derivation to prove that they are the same (really good course BTW). Another big reason we often prefer to use Bayesian methods is that it allows us to incorporate uncertainty in our parameter estimates which is particularly useful when forecasting.
How to fit structural time series with BSTS R?
This post summarizes the bsts R package, a tool for fitting Bayesian structural time series models. These are a widely useful class of time series models, known in various literatures as “structural time series,” “state space models,” “Kalman filter models,” and “dynamic linear models,” among others.
Which is the best introduction to Bayesian var estimators?
Koop and Korobilis (2010) provide a very good introduction to Bayesian VAR estimators. As already mentioned, Bayesian inference can be algebraically demanding. However, Bayesian estimators for linear VAR models can be implemented in a straightforward manner.