Year: 2019

Using GAUSS Packages [Complete Guide]

GAUSS packages provide access to powerful tools for performing data analysis. This guide covers all you need to know to get the most from GAUSS packages including:
  • What is a GAUSS package
  • Where to find GAUSS packages
  • What is included in GAUSS packages
  • How to use GAUSS packages

Fundamental Bayesian Samplers

The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.

Marginal Effects of Linear Models with Data Transformations

We use regression analysis to understand the relationships, patterns, and causalities in data. Often we are interested in understanding the impacts that changes in the dependent variables have on our outcome of interest. However, not all models provide such straightforward interpretations. Coefficients in more complex models may not always provide direct insights into the relationships we are interested in. In this blog, we look more closely at the interpretation of marginal effects in three types of models:
  • Purely linear models.
  • Models with transformations in independent variables.
  • Models with transformations of dependent variables.

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