Bayesian analyses are becoming increasingly popular in many areas within life sciences, but as yet there is no text that explains the ideas at a level that researchers with minimal background knowledge can understand. This is achieved in this book thanks to a focus on methodology, rather than theory, with many multiple real-world examples, problems and case studies. There will be an emphasis on the flexibility the Bayesian framework provides for fitting complex models. This accurately reflects the data that has been collected, and also makes it easier to estimate the parameters of scientific interest. This book can be also useful for statisticians working in computational biology and evolutionary genetics or to researchers in social sciences. Any sort of reader will benefit of the plenty of exercises and practical examples. Finally, to enhance the learning experience, a Code for the BUGS package will be provided, in the aim to carry out computational Bayesian analysis via a Windows platform package.