By Peter D.,Congdon
The use of Markov chain Monte Carlo (MCMC) tools for estimating hierarchical versions consists of complicated info buildings and is frequently defined as a innovative improvement. An intermediate-level remedy of Bayesian hierarchical types and their purposes, Applied Bayesian Hierarchical Methods demonstrates some great benefits of a Bayesian method of info units related to inferences for collections of similar devices or variables and in equipment the place parameters might be taken care of as random collections.
Emphasizing computational matters, the booklet offers examples of the next software settings: meta-analysis, info based in area or time, multilevel and longitudinal information, multivariate information, nonlinear regression, and survival time facts. For the labored examples, the textual content commonly employs the WinBUGS package deal, permitting readers to discover substitute probability assumptions, regression constructions, and assumptions on earlier densities. It additionally comprises BayesX code, that is quite valuable in nonlinear regression. to illustrate MCMC sampling from first rules, the writer comprises labored examples utilizing the R package.
Through illustrative info research and a spotlight to statistical computing, this e-book makes a speciality of the sensible implementation of Bayesian hierarchical equipment. It additionally discusses numerous matters that come up whilst utilising Bayesian options in hierarchical and random results models.
Read Online or Download Applied Bayesian Hierarchical Methods PDF
Similar probability & statistics books
A thought-provoking examine statistical studying thought and its function in realizing human studying and inductive reasoning A joint exercise from major researchers within the fields of philosophy and electric engineering, An common creation to Statistical studying concept is a entire and available primer at the quickly evolving fields of statistical trend acceptance and statistical studying conception.
The purpose of this quantity is to supply an intensive account of the latest advances in data for discretely saw Lévy strategies. nowadays, information for stochastic procedures is a full of life subject, pushed by means of the desires of assorted fields of program, reminiscent of finance, the biosciences, and telecommunication.
This guide represents a milestone within the development of information Envelopment research (DEA). Written through specialists who're frequently significant members to DEA concept, it incorporates a number of chapters that signify the present cutting-edge in DEA learn. issues comprise distance capabilities and their price duals, cross-efficiency measures in DEA, integer DEA, weight regulations and creation trade-offs, aspect research in DEA, scale elasticity, benchmarking and context-dependent DEA, fuzzy DEA, non-homogenous devices, partial input-output relatives, large potency, therapy of bad measures, translation invariance, stochastic nonparametric envelopment of information, and international frontier index.
"There is not anything love it out there. .. no others are as encyclopedic. .. the writing is exemplary: uncomplicated, direct, and useful. "—George W. Cobb, Professor Emeritus of arithmetic and statistics, Mount Holyoke collage Written in a right away and transparent demeanour, vintage subject matters at the historical past of contemporary Mathematical data: From Laplace to newer occasions offers a complete consultant to the background of mathematical information and info the main effects and the most important advancements over a 200-year interval.
- Lévy Matters V: Functionals of Lévy Processes: 5 (Lecture Notes in Mathematics)
- Designing Public Policies: An Approach Based on Multi-Criteria Analysis and Computable General Equilibrium Modeling: 642 (Lecture Notes in Economics and Mathematical Systems)
- Multilevel and Longitudinal Modeling Using Stata, Volumes I and II
- Business Statistics: Communicating with Numbers (Irwin Statistics)
- Advances in Applied Mechanics: 44
Extra resources for Applied Bayesian Hierarchical Methods
Applied Bayesian Hierarchical Methods by Peter D.,Congdon