bayess: Bayesian Essentials with R. bayess contains a collection of functions that allows the reenactment of the R programs used in the book ”Bayesian Essentials with R” (revision of ”Bayesian Core”) without further programming. R code being available as well, they can be modified by the user to conduct one’s own simulations.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
- Tancredi, Andrea; Steorts, Rebecca; Liseo, Brunero: A unified framework for de-duplication and population size estimation (with discussion) (2020)
- Austad, Haakon Michael; Tjelmeland, Håkon: Approximate computations for binary Markov random fields and their use in Bayesian models (2017)
- Ly, Alexander; Verhagen, Josine; Wagenmakers, Eric-Jan: An evaluation of alternative methods for testing hypotheses, from the perspective of Harold Jeffreys (2016)
- Vallet, Ana Corberán: Book review of: J.-M. Marin and C. P. Robert, Bayesian essentials with R. 2nd ed. (2016)
- Launay, Tristan; Philippe, Anne; Lamarche, Sophie: Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting (2015)
- Moores, Matthew T.; Hargrave, Catriona E.; Deegan, Timothy; Poulsen, Michael; Harden, Fiona; Mengersen, Kerrie: An external field prior for the hidden Potts model with application to cone-beam computed tomography (2015)
- Marin, Jean-Michel; Robert, Christian P.: Bayesian essentials with R (2014)