Scala and Spark for Big Data Analytics

How does it work?

Here we discuss a general setup for a statistical inference problem. At the first place, from the data, we estimate the desired quantity and there might be unknown quantities too that we would like to estimate. It could be simply a response variable or predicted variable, a class, a label, or simply a number. If you are familiar with the frequentist approach, you might know that in this approach the unknown quantity say θ is assumed to be a fixed (nonrandom) quantity that is to be estimated by the observed data.

However, in the Bayesian framework, an unknown quantity say θ is treated as a random variable. More specifically, it is assumed that we have an initial guess about the distribution of θ, which is commonly referred ...