It is well known that standard frequentist inference breaks down in IV regressions with weak instruments. Bayesian inference with diffuse priors suffers from the same problem. We show that the issue ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

In my practice, I find most people involved with advanced analytics, such as predictive, data science, and ML, are familiar with the name Bayes, and can even reproduce the simple theorem below. Still, ...

This paper is a generalization of earlier studies by Ferreira (1975) and Holbert and Broemeling (1977), who used improper prior distributions in order to make informal Bayesian inferences for the ...

This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ...

Approach developed at the Texas A&M School of Public Health offers promising new knowledge on idiopathic pulmonary fibrosis pathways Texas A&M University A new statistical technique developed by a ...