Introduction To Applied Bayesian Statistics And Estimation For Social Scientists Covers The Complete Process Of Bayesian Statistical Analysis In Great Detail From The Development Of A Model Through The Process Of Making Statistical Inference The Key Feature Of This Book Is That It Covers Models That Are Most Commonly Used In Social Science Research Including The Linear Regression Model, Generalized Linear Models, Hierarchical Models, And Multivariate Regression Models And It Thoroughly Develops Each Real Data Example In Painstaking DetailThe First Part Of The Book Provides A Detailed Introduction To Mathematical Statistics And The Bayesian Approach To Statistics, As Well As A Thorough Explanation Of The Rationale For Using Simulation Methods To Construct Summaries Of Posterior Distributions Markov Chain Monte Carlo MCMC Methods Including The Gibbs Sampler And The Metropolis Hastings Algorithm Are Then Introduced As General Methods For Simulating Samples From Distributions Extensive Discussion Of Programming MCMC Algorithms, Monitoring Their Performance, And Improving Them Is Provided Before Turning To The Larger Examples Involving Real Social Science Models And Data
Is a well-known author, some of his books are a fascination for readers like in the Introduction to Applied Bayesian Statistics and Estimation for Social Scientists book, this is one of the most wanted Scott M. Lynch author readers around the world.
- 357 pages
- Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
- Scott M. Lynch
- 11 November 2018 Scott M. Lynch