Skip to Content
Applied Bayesian Modelling, 2nd Edition
book

Applied Bayesian Modelling, 2nd Edition

by Peter Congdon
July 2014
Intermediate to advanced
464 pages
16h 27m
English
Wiley
Content preview from Applied Bayesian Modelling, 2nd Edition

Index

  1. Accelerated failure time (AFT) model
  2. Accelerated hazards
    1. AFT model
    2. bone marrow transplant
    3. proportional hazards model
    4. Weibull hazard
  3. Akaike information criterion (AIC)
  4. ARCH model
  5. ARD prior. See Automatic relevance determination (ARD) prior
  6. ARMA models. See Autoregressive and moving average (ARMA) models
  7. Augmented data regression
  8. Automatic relevance determination (ARD) prior
  9. Autoregressive and moving average (ARMA) models
    1. Bayesian priors
      1. BUGS code
      2. normal– gamma conjugate priors
      3. outliers/shifts
      4. Schur's theorem
      5. stationarity enforcement
    2. dependent errors
    3. distributed lag regression
    4. estimation and forecasting
    5. likelihood model
    6. vector autoregressive models
  10.  
  11. Bayesian evidence synthesis. See Meta-analysis
  12. Bayesian information criterion (BIC)
  13. Bayesian regression
  14. Bernoulli parameters
  15. Beta-binomial hierarchical model
  16. Bias– variance trade-off
  17. BIC. See Bayesian information criterion (BIC)
  18. Binary and binomial regression
    1. Cauchy priors
    2. cross-validatory (CV) predictive density
    3. DAP prior
    4. distribution function
    5. evidence-based priors
    6. latent outcome approach
    7. link function
    8. MCMC iteration
    9. posterior density
    10. predictive/discriminatory value
    11. scaling nonbinary predictors
  19. Binary panel data
    1. employment transitions
    2. latent continuous variables
    3. logit link
    4. MCMC sampling
    5. permanent subject effects
    6. probability of success
    7. truncated normal sampling
  20. Bivariate meta-analysis
  21. Box– Cox transform
  22.  
  23. CESD depression index
  24. Conditional autoregressive (CAR) prior
  25. Conditional predictive ordinate (CPO)
    1. cross-validation ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

AirBnbBlueOriginElectronic ArtsHomeDepotNasdaqRakutenTata Consultancy Services

QuotationMarkO’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
Julian F.
Head of Cybersecurity
QuotationMarkI wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.
Addison B.
Field Engineer
QuotationMarkI’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.
Amir M.
Data Platform Tech Lead
QuotationMarkI'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Mark W.
Embedded Software Engineer

You might also like

Case Studies in Bayesian Statistical Modelling and Analysis

Case Studies in Bayesian Statistical Modelling and Analysis

Clair L. Alston, Kerrie L. Mengersen, Anthony N. Pettitt
Bayesian Analysis of Stochastic Process Models

Bayesian Analysis of Stochastic Process Models

David Insua, Fabrizio Ruggeri, Mike Wiper

Publisher Resources

ISBN: 9781118895061Purchase book