This is another post in the series of model checking posts. Previously we looked at which aspects of the data and model are compatible, using posterior predictive checks. Once we have selected a model or a set of models for the data, we would like to score and compare them. One aspect of comparison using … Continue reading Model Checking: Scoring and Comparing Models

# Month: May 2017

# Model Checking: Posterior Predictive Checks

Once a model is fit and parameters estimated, we would look at how well the model explains the data and what aspects of the data generation process in nature are not captured by the model. Most of the material covered in this post follows the examples from: [1] Gelman, A., Carlin, J. B., Stern, H. S., … Continue reading Model Checking: Posterior Predictive Checks

# Parameter Estimation

In statistical or mathematical models our aim is to look at the data and estimate the parameters and uncertainty of those estimations. Generally speaking, looking at a data set, we wish to choose a likelihood/noise/sampling distribution, that fits the data. A distribution requires some parameters, Θ, e.g. a normal distribution (which is a very common error/noise … Continue reading Parameter Estimation

# Mixture Distributions and Model Comparison

The following text and code snippets show examples from two books on Bayesian Data Analysis: [1] Kruschke, J. K. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan, second edition. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition. http://doi.org/10.1016/B978-0-12-405888-0.09999-2 [2] Albert, J., Gentleman, R., Parmigiani, G., & Hornik, K. … Continue reading Mixture Distributions and Model Comparison