Earlier, we saw how John K. Kruschke explained Bayesian inference in his book, “Doing Bayesian Data Analysis”. Today, I will present another elegant description from the YouTube channel “rasmusab”. He explains Basian Data Analysis as:
“A method to figure out unknowns, known as parameters, using”
- Data
- A generative model: a mathematical formulation that can give simulated data from the input of parameters.
- Priors: information for the model before seeing the data
So, the objective is to estimate a reasonable set of parameter values that could have generated the data, as observed. And it is done in this fashion:
Plug in a parameter value
Run it through the generative model
Get out the simulated data
Accept only those parameter values that gave the simulated data = observed data.
Reference
Doing Bayesian Data Analysis by John K. Kruschke
Introduction to Bayesian data analysis – part 1: What is Bayes?: rasmusab
