Jesper Fischer Ehmsen

Jesper Fischer Ehmsen

Cognitive Scientist

Blog posts

▸ Starting Off: Bayesian Workflow

Data Simulation

10 minute read

Published:

  • Bayesian Workflow

This R markdown document gives an idea of how data simulation fits into the Bayesian workflow and how to perform this in R.

Model Fitting To Simulated Data

19 minute read

Published:

  • Bayesian Workflow

This R markdown document showcases how one can fit models to simulated data in R and Stan.

Priors

20 minute read

Published:

  • Bayesian Workflow

This R markdown is for explaining priors in stan.

Diagnostics

53 minute read

Published:

  • Bayesian Workflow

This R markdown document is an introduction of how to diagnose Stan models.

Hierarchical Models

31 minute read

Published:

  • Bayesian Workflow

This R markdown is an introduction to fitting Hierarchical models in stan.

Parameter Recovery

19 minute read

Published:

  • Bayesian Workflow

This R markdown is for conducting parameter recovery.

Distributions

26 minute read

Published:

  • Bayesian Workflow

The following markdown is an insight into how i think about distributions and the use of these in statistical and computational modeling.

Multivariate Priors And Correlations

28 minute read

Published:

  • Bayesian Workflow

This R introduces and expands on hierarchical modeling with variance co-variance matrices and multivariate distributions.

▸ Multivariate Copula Modeling

Multivariate Normals

8 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for explaining simple copula modeling in stan.

Continous Copulas

4 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for Showcasing the continous gaussian copula in non-gaussian marginals.

Conditional Marginals

5 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for Showcasing of continous non gaussian copula with conditional marginals

Discrete Copulas

6 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for Showcasing of mixed marginals (continous and discrete) with a gaussian copula.

Building Multivariate Models

16 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for showcasing how one can use copulas to build cogntive computational multivariate models and what can be gained from it. With an example of decision making with confidence ratings.

Discrete Confidence

7 minute read

Published:

  • Multivariate Copula Modeling

This R markdown is for Showcasing the continous gaussian copula in non-gaussian marginals.