similar to: What Bayesian Framework?

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

Dear R-users, I would like to announce the release of the new package JMbayes available from CRAN (http://CRAN.R-project.org/package=JMbayes). This package fits shared parameter models for the joint modeling of normal longitudinal responses and event times under a Bayesian approach using JAGS, WinBUGS or OpenBUGS. The package has a single model-fitting function called jointModelBayes(),

Dear R-users, I would like to announce the release of the new package JMbayes available from CRAN (http://CRAN.R-project.org/package=JMbayes). This package fits shared parameter models for the joint modeling of normal longitudinal responses and event times under a Bayesian approach using JAGS, WinBUGS or OpenBUGS. The package has a single model-fitting function called jointModelBayes(),

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Dear List: I have uploaded version 1.0-4 of DPpackage on CRAN. Since the first version (1.0-0), I have not communicated the improvements of the package. I'll use this email to summarize its current status. The name of the package is motivated by the Dirichlet process. However, DPpackage tries to be a general package for Bayesian nonparametric and semi-parametric data analysis. So

Hola comunidad, Les consulto mi duda. Quisiera generar N números aleatorios tal que su suma esté predeterminada a un valor k. Sé que con la distribución Dirichlet, por ejemplo, se podrían obtener números aleatorios tal que su suma es la unidad. Por ejemplo MCMCpack::rdirichlet(1,rep(10,7)) genera 7 números tal que su suma es 1 (el parámetro rep(10,7) es cualquier cosa). Pero me pregunto si se

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

Dear useRs, French reading people among you might be interested by the following book: Noel, Y. (2013). Psychologie statistique avec R [Statistical psychology with R, in French], coll. PratiqueR, Paris: Springer. http://www.springer.com/psychology/book/978-2-8178-0424-8 This book provides a detailed presentation of all basics of statistical inference for psychologists, both in a fisherian

I'm using the dump command to pass data to WinBUGS/OpenBUGS/JAGS and have run into a problem. Here is some R-code: foo <- array(1:6, dim=c(2,3)) dump('foo', file='dumpdata.R') dump('foo', file='dumpdata.R', append=TRUE, evaluate=TRUE) foo2 <- array(c(2,3,5,7,9,7,5,3), dim=c(2,4)) dump('foo2', file='dumpdata.R', append=TRUE) And here is

I try to do a test for dirichlet process for Multivariate normal, but Winbugs always says "expected multivariate node", does that mean I miss something at initialization? I will really appreciate the help to solve this problem Here is the R code, and Winbugs code. model { for(i in 1:N){ y[i,1:2] ~ dmnorm(mu[i,],tau[i,,]) S[i] ~ dcat(pi[]) mu[i,1:2] <- mu.star[S[i],]

I installed wine 1.2.3 on Mac OS X 10.6.8 with the hopes of getting OpenBUGs running, but no success. It seems like OpenBUGs installs properly, but when I try to open it, I get the following: wine: cannot find L"C:\\windows\\system32\\OpenBUGS.exe What does this mean/how can I fix it?

Dear All, Could we specify the multinomial distribution in R when doing Bayesian data analysis through R2WinBUGS? See below for my issues. t2[i,1:2] ~ dmulti(p2[i,1:2],n2[i]) #t2[] is a matrix with two columns, the problem is how to specify it in R p2[i,1] <- (p[i] * s1[t[i]] * s2[t[i]] + (1 - p[i]) * (1 - c1[t[i]]) * (1 - c2[t[i]]))/p1[i] p2[i,2] <- (p[i] * s1[t[i]] * (1 - s2[t[i]]) +(1 -

Code to reproduce > kernel("dirichlet") Error in kernel("dirichlet") : argument "r" is missing, with no default The help says r??? the kernel order for a Fejer kernel. Thanks to update the help. Best, Samuel

Hello ALL! Some times ago I started to learn and play with Bayesian stuffs. Many advice use of WinBUGS for Bayesian inference Using Gibbs Sampler. However, WinBUGS is discontinued, and now, development is under OpenBUGS. I wasn't lazy, so I installed both and tried out. In more than 90% of cases they give comparable outcome. But in few cases I got substantial differences. Recently, I read nice

Dear list members, I want to draw surfaces of Dirichlet distributions with different parameter settings. My code is the following: #<begin code> a1 <- a2 <- a3 <- 2 #a2 <- .5 #a3 <- .5 x1 <- x2 <- seq(0.01, .99, by=.01) f <- function(x1, x2){ term1 <- gamma(a1+a2+a3)/(gamma(a1)*gamma(a2)*gamma(a3)) term2 <- x1^(a1-1)*x2^(a2-1)*(1-x1-x2)^(a3-1)

Dear Wine-friends, I was wondering if any of you would have a clue around why WinBUGS (http://www.mrc-bsu.cam.ac.uk/bugs/), a nifty Markov chain Monte Carlo sampler widely used in Bayesian statistical modelling, no longer works when run through any version of WINE newer than 1.1.12. I have encountered this issue of my machines at home (which runs on Zenwalk 6.0) and work (Mandriva 2008.1) a

A naive question from a non-statistician: I'm looking into running a Bayesian analysis of a model with high dimensionality. It's not a standard model (the likelihood requires a lot of code to implement), and I'm using a Linux machine. Was wondering if someone has any thoughts on what the advantages of OpenBugs are as opposed to just R (or should I be thinking WinBUGS under Wine?)?

DeaR UseRs, I have the following code (see below). It is working as expected although I have two problems/questions: - how can I set the size of the graph? It may sound silly, but I couldnt figure that out - is it possible to export this small "interactive aplication" to html or a latex document, or maybe pdf? Thank you for your attentions. Best wishes: Daniel the code: