similar to: Naive Gibbs Sampling with Metropolis Steps (pkg: gibbs.met)

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

Hi Everybody, A new package called ``Bayesian Prediction with High-order Interactions'' is available from CRAN. The description of this package is as follows" "This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate

Dear list, After running for a while, it crashes and gives the following error message: can anybody suggest how to deal with this? Error in if (ratio0[i] < log(runif(1))) { : missing value where TRUE/FALSE needed ################### original program ######## p2 <- function (Nsim=1000){ x<- c(0.301,0,-0.301,-0.602,-0.903,-1.208, -1.309,-1.807,-2.108,-2.71) # logdose

Dears, I have the below code for metropolis of the GLM logit (logistic regression) using a flat prior. Can someone help me modify the prior so that the model becomes hierarchical by using a flat prior for mu and sigma, the derived density for beta ~ N(mu, sigma^2)? Actually I took my code from a teacher that posted on the internet and modified it to the GLM logit but I can't adapt it to the

I have wrote a small Linux Shell command for implementing Interlock Protocol which is known as a cryptographic protocol that resistant to man-in-the-middle attack. Here is the steps of interlock protocol: *(1)* Alice send her public key to Bob *(2)* Bob send his public key to Alice. *(3)* Alice encrypts her message using Bob's public key. Then she sends half of that encrypted message to

I'm testing some different formulations of pairwise interaction point processes in Spatstat (version 1.11-6) using R 2.5.0 on a Windows platform and I wish to simulate them using the Metropolis-Hastings algorithm implemented with Spatstat. Spatstat utilizes Fortran77 code with the preprocessor RatFor to do the Metropolis-Hastings MCMC, but the Makefile is more complicated than any I have

Is there a way to recover Metropolis-step acceptance rates AFTER completing posterior draws? The immediate application is in the probit.bayes and logit.bayes models used by Zelig... which I believe is merely calling MCMCpack. So one strategy, to which I am fixing to resort, is to call, say, MCMClogit with verbose set to mcmc (or mcmc divided by an integer) and then look at my screen.

Hello all, could you tell where is an example of metropolis hasting? Thank you! Tania Sent from my iPod

Hi all, I'm trying to write a MH algorithm in R for a standard normal distribution, I've been trying for a good week or so now with multiple attempts and have finally given up trying to do it on my own as I'm beginning to run out of time for this, would somebody please tell me what is wrong with my latest attempt: n=100 mu=0 sigma=1 lik<-function(theta) exp(((theta-mu)^2)/2*sigma)

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

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

I'm new at R ... I've not done for loops in R - so this is very new to me. One of our students has a data frame that contains two columns data 1. unixtime time of an event (in unix time - #of seconds) 2. duration of event in seconds. We need to create new data - the unixtime (seconds) that these events occurred. We want to create a for loop (or nested for loops) that goes through the

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Hi; There is likely a supported way around this problem, but it wasn't immediately apparent to me. So, I created the enclosed patch to fix my problem. What I would like to do is the following: - run samba in "security = domain" - not use trusted domains, but allow people to connect from other domains - not maintain a local encrypted password file for samba, but instead use our

Following is my code, can some one help on the error at the bottom? > mh<-function(iterations,alpha,beta){ + data<-read.table("epidemic.txt",header = TRUE) + attach(data, warn.conflicts = F) + k<-97 + d <- (sqrt((x-x[k])^2 + (y-y[k])^2)) + p <- 1-exp(-alpha*d^(-beta)) + p.alpha<-1 - exp(-3*d^(-beta)) + p.beta <- 1 - exp(alpha*d^(-2)) +

Hi, When i try to inser this on LDAP database, i get this error: "ldapadd: invalid format (line 14) entry: "uid=spessoa,ou=users,ou=accounts,dc=telbit,dc=pt"" I can't see nothing wrong. The .ldif file follows my signature. Any help would be appreciated. Warm Regards, M?rio Gamito -- dn: dc=telbit,dc=pt objectClass: top objectClass: dcObject objectClass: organization o:

Hi, Do any body know which packages of R I need to go for the below topics? 1. Monte Carlo Markov chain (MCMC) 2. Gibbs Sampling 3. Metropolis Hastings Thanks in advance... Shubha [[alternative HTML version deleted]]

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

Dear all, The new version of AdMit (version 1.01-01) is now available from CRAN. SUMMARY The package provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings

Hi Paul Is there a concrete working example somewhere that shows how to use these to do an animation on Windows (R Gui &/or RStudio) using base R plot() and friends? I have several old examples somewhere that used to work (R < ~ 3), but now no longer work as before. Date: Mon, 25 Mar 2024 10:43:29 +1300 From: Paul Murrell <paul at stat.auckland.ac.nz<mailto:paul at