Here you will find daily news and tutorials about R, contributed by over 750 bloggers.
There are many ways to follow us - By e-mail:On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

Bayesian Inference is a way of combining information from data with things we think we already know. For example, if we wanted to get an estimate of the mean height of people, we could use our prior knowledge that people are generally between 5 and 6 feet tall to inform the results from the data we collect. If our prior is informative and we don't have much data, this will help us to get a better estimate. If we have a lot of data, even if the prior is wrong (say, our population is NBA players), the prior won't change the estimate much. You might say that including such "subjective" information in a statistical model isn't right, but there's subjectivity in the selection of any statistical model. Bayesian Inference makes that subjectivity explicit.