Introduction to Bayesian Statistics

The aim of this thesis is to cover the basics of Bayesian inference. Bayesian logic is to consider parameter as a random variable with specific prior distribution. Prior distrubution can be chosen from wide range of possibilities. In this thesis miscellaneous choices of prior distribution are discussed and are followed with many examples. The another part of thesis concerns with building Bayesian point and interval estimates. Everything is compared to classical approach towards statistics. Last section shows the application of previous topics on real data