The main objective of this course is to provide a strong mathematical and conceptual foundation in the methods of Bayesian statistics, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in research. Bayesian methods which allow for inclusion of relevant problem-specific knowledge in to the formation of one’s statistical model.

Contents

Introduction to Bayesian Statistics, Benifits of Bayesian Statistics, Application of Bayesian Statistics, Prior information, prior distributions and its types, methods of elicitation of prior distributions, Posterior distributions: The posterior mean, median and mode,  Bayes estimators under loss functions and variances of univariate and bivariate posterior distributions, noninformative priors: methods of elicitation of noninformative factor; Bayesian Hypothesis Testing: Bayes factor; the highest density region; posterior probability of the hypothesis.

Recommended Books

1. Bolstad, W. M., & Curran, J. M. (2016). Introduction to Bayesian statistics. John Wiley & Sons.
2. Berger, J. O. (2013). Statistical decision theory and Bayesian analysis. Springer Science & Business Media.

Distribution of Marks:

Mid Exam:           30
Final exam:         50
Sessional (Assignment,Presentation,Participation,Attendance,Quizes)    20

Scheduled on:      
MSc:     Tuesday(10:00-11:00)      Wednesday(12:00-1:00)    Thursday(8:00-9:00)