Introduction To Bayesian Statistics

Kennie Nybo Pontoppidan has just completed a course on Bayesian statistics:

Last month I finished a four-week course on Bayesian statistics. I have always wondered why people deemed it hard, and why I heard that the computations quickly became complicated. The course wasn’t that hard, and it gave a nice introduction to prior/posterior distributions and I many cases also how to interpret the parameters in the prior distribution as extra data points.

An interesting aspect of Bayesian statistics is that it is a mathematically rigorous model, with no magic numbers such as the 5% threshold for p-values. And I like the way it naturally caters sequential hypothesis testing with where the sample size of each iteration is not fixed in advance. Instead data are evaluated and used to update the model as they are collected.

Check out Kennie’s explanation as well as the course.  I also went through Bayes’ Theorem not too long ago, which is a good introduction to the topic if you’re unfamiliar with Bayes’s Law.

Related Posts

Tidy Anomaly Detection With Anomalize

Abdul Majed Raja walks us through an example using the anomalize package: One of the important things to do with Time Series data before starting with Time Series forecasting or Modelling is Time Series Decomposition where the Time series data is decomposed into Seasonal, Trend and remainder components. anomalize has got a function time_decompose() to perform the same. […]

Read More

Uploading Data Sets To Azure ML From R

Leila Etaati continues her series on the Azure ML R package by showing how to upload a data set: There is a function in AzureML package name “workspace” that creates a reference to an AzureML Studio workspace by getting the authentication token and workspace id as below: 1 ws <– workspace( id , auth  ) to […]

Read More


September 2017
« Aug Oct »