Bayesian Essentials with R
Gibbs sampler for a generic mixture posterior distribution
Metropolis-Hastings for the logit model under a flat prior
Metropolis-Hastings for the log-linear model under a flat prior
Metropolis-Hastings for the probit model under a flat prior
Estimation of a hidden Markov model with 2 hidden and 4 observed state...
Metropolis-Hastings with tempering steps for the mean mixture posterio...
Metropolis-Hastings for the logit model under a noninformative prior
Accept-reject algorithm for the open population capture-recapture mode...
log-likelihood associated with an AR(p) model defined either through i...
Metropolis--Hastings evaluation of the posterior associated with an AR...
bank dataset (Chapter 4)
Bayesian linear regression output
Pine processionary caterpillar dataset
Non-standardised Licence dataset
DNA sequence of an HIV genome
European Dipper dataset
Eurostoxx50 exerpt dataset
Gibbs sampler and Chib's evidence approximation for a generic univaria...
Gibbs sampler for the two-stage open population capture-recapture mode...
Gibbs sampling for the Arnason-Schwarz capture-recapture model
Gibbs sampler on a mixture posterior distribution with unknown means
Metropolis-Hastings for the log-linear model under a noninformative pr...
Metropolis-Hastings for the probit model under a noninformative prior
Metropolis-Hastings for the Ising model
Gibbs sampler for the Ising model
Laiche dataset
Log-likelihood of the logit model
Log of the posterior distribution for the probit model under a noninfo...
Log of the likelihood of the log-linear model
Log of the posterior density for the log-linear model under a noninfor...
log-likelihood associated with an MA(p) model
Metropolis--Hastings evaluation of the posterior associated with an MA...
Grey-level image of the Lake of Menteith
Bayesian model choice procedure for the linear model
Normal dataset
Posterior expectation for the binomial capture-recapture model
Posterior probabilities for the multiple stage capture-recapture model
Posterior probabilities for the Darroch model
Graphical representation of a normal mixture log-likelihood
Gibbs sampler for the Potts model
Metropolis-Hastings sampler for a Potts model with ncol classes
Coverage of the interval by the Beta cdf
Log-likelihood of the probit model
Log of the posterior density for the probit model under a non-informat...
Random generator for the Dirichlet distribution
Image reconstruction for the Potts model with six classes
Recursive resolution of beta prior calibration
Approximation by path sampling of the normalising constant for the Isi...
Bound for the accept-reject algorithm in Chapter 5
Random simulator for the truncated normal distribution
Number of neighbours with the same colour
Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.