Introduction
Statistical inference is the process of drawing inferences about population parameters on the basis of information obtained from sample drawn from population. This subject allow readers to make generalizations to a large number of individuals based on information from a limited number of objects.This subject is based on main three pillars of statistics i.e. Probability theory, sampling distributions statistical inference. There are many modes of performing inference including statistical modeling, data oriented strategies and involvment of designs and randomization in analyses. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. The main objective of this course is to provide a strong mathematical and conceptual foundation in the methods of statistical inference, with an emphasis on practical aspects of the interpretation and communication of statistically based conclusions in research. Statistical estimation is concerned with the best estimating a value or range of values for a particular population parameter. It deals with the estimation of parameters, properties of good point estimator and its methods. It also discusses the parameter estimation of different probability distributions and their efficiency. Bayesian Statistics and its comparison with classical estimation approach are part of the content.
Table of Contents
Recommended Texts
Week  Topics and Readings  Book name 
1  Introduction to Statistical Inference 
Statistical inference

2  Point of estimation, properties of good point estimators  Statistical Inference 
3  Unbiasedness, Consistency, order distribution  Introduction to mathematical statistics 
4  Sufficiency, Minimal sufficiency and joint sufficiency  Introduction to mathematical statistics 
5  Exponential family  Introduction to mathematical statistics 
6  Fisher information  Introduction to mathematical statistics 
7  Cramer Rao Lower Bound  Introduction to mathematical statistics 
8  Rao Blackwell and Lehmann Scheffe Theorem  Introduction to mathematical statistics 
10  Methods of estimation  Introduction to mathematical statistics 
11  Least squares and moments  Introduction to mathematical statistics 
12  Bayes method  Introduction to mathematical statistics 
13  Comparison of Bayesian and Classical methods  Introduction to mathematical statistics 
14  Prior distribution and liklihood function  Statistical Inference 
15  Posterior distributions generation  Statistical Inference 
16  Benefits of Bayesian methods  Statistical Inference 
17  Overview of previous  Statistical Inference 
Statistical Prerequisities: 3(30)
Time Table:
MSC (3rdReg)
Distribution of Marks:
Mid Exam: 30
Final exam: 50
Sessional (Assignment,Presentation,Participation,Attendance,Quizes) 20