similar to: Bayesian inference: Poisson distribution with normal (!) prior

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){

Hi all, I appreciate the help this list has given me before. I have a question which has been perplexing me. I have been working on doing a Bayesian calculating inserting studies sequentially after using a non-informative prior to get a meta-analysis type result. I created a function using three iterations of this, my code is below. I insert prior mean and precision (I add precision manually

Hello all, Im on the latest fresh installed centos7 with samba / winbind samba: Architektur : x86_64 Version : 4.2.10 Ausgabe : 6.el7_2 Name : samba-winbind Architektur : x86_64 Version : 4.2.10 Ausgabe : 6.el7_2 doing wbinfo -i user is working doing wbinfo -g is working doing wbinfo -u is not working and I get in winbind log: NT_STATUS_IO_TIMEOUT and Im not able to access

Hi all, I have the data from the posterior distribution for some parameter. I want to find the 95% credible interval. I think "t.test(data)" is only for the confidence interval. I did not fine function for the Bayesian credible interval. Could some one suggest me? Thanks [[alternative HTML version deleted]]

Hi all. Are there any R functions around that do quick logistic regression with a Gaussian prior distribution on the coefficients? I just want posterior mode, not MCMC. (I'm using it as a step within an iterative imputation algorithm.) This isn't hard to do: each step of a glm iteration simply linearizes the derivative of the log-likelihood, and, at this point, essentially no

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

Hi All: Recently I am researching my dissertation about the quantile model selection by bayesian approach. I have the dependent variable(return) and 16 independent variables and I need to select the best variable for each quantile of return. And the method I used is the bayesian approach, which is based on calculating the posterior distibution of model identifier. In other words, I need to obtain

In the following code I have created the posterior density for a Bayesian survival model with four parameters. However, when I try to use the adapt function to perform integration in four dimensions (on my old version of R I get an error message saying that I have applied a non-function, although the function does work when I type kernel2(param0, theta0), or on the newer version of R the computer

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Hi, I am using Partek for LDA analysis. For a binary response variable, it generates 2 discriminant functions, one for each of the 2 levels of the response variable. And I can simply calculate 2 discriminant scores (say d1 and d2) for each sampples using the 2 discriminant functions, then I can use the following formula to compute the posterior probability for the sample:

Dear Members i'm trying to access different packages used for Bayesian analysis, but failed to integrate after making the likelihood of the model the model like this a= exp(b)/summation(exp(b)) where 'b' = half of the natural log of 'a' please If some one knows about this type of integration for posterior distribution then pleae inform me SYED ADIL HUSSAIN MPHIL SCHOLER QAU,

Good afternoon/morning readers. This is the first time I am trying to run some Bayesian computation in R, and am experiencing a few problems. I am working on a Poisson model for cancer rates which has a conjugate Gamma prior. 1) The first question is precisely how I work out the parameters. #Suppose I assign values to theta with *seq()* *theta<-seq(0,1,len=500)* #Then I try out the

Hello, i only got a small problem. i try to create automatic new dataframes, or png?s. the main problem i got is: how can i create automatic a new name for a file (read out by simply "for") - i tried to use "(paste...) but theres an errormessage, about a wrong declination. R told it is as.character, but need as.Real. Should i use another method than "paste"? i tried as

Thanks Jan. I've been trying to obtain an English copy of the Dovecot book for months, prior to starting this project. So far, I just can't find a copy. It's too bad that the author/publisher won't do a second printing or, if they're not interested in making any more money, then release it to the public domain as a PDF. Very frustrating. Michael > -----Original

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 Friends, According to Gelman et al (2003), "...Bayesian P-values are defined as the probability that the replicated data could be more extreme than the observed data, as measured by the test quantity p=pr[T(y_rep,tetha) >= T(y,tetha)|y]..." where p=Bayesian P-value, T=test statistics, y_rep=data from replicated experiment, y=data from original experiment, tetha=the function

Dear friends, Over the past weeks, I have been asking a lot of questions about how to use R in Bayesian analysis. I am brand new to R, but I am very pleased with it. I started with winbugs but I found winbugs to be a limited software, not bad but has several limitations. By contrast, R allows the analyst to tackle any problem with a huge set of tools for any kind of analysis. I love R. In

Greetings all, I'm a newcomer to Bayesian stats, and I'm trying to calculate the Deviance Information Criterion "by hand" from some MCMC output. However, having consulted several sources, I am left confused as to the exact terms to use. The most common formula can be written as DIC = 2*Mean(Deviance over the whole sampled posterior distribution) - Deviance(Mean

I am attempting to use optim to solve a neural network problem. I would like to optimize coefficients that are currently stored in a matrix Y=270 x 1 X= 27- x 14 b1= 10x14 b2= 11x1 V= 10 x 14 set of prior variances. I have the following function: posterior.mode1=function(y,X,b_0,b2,V) { log.like=function(b1) { a_g=compute(b1) z_g=tanh(a_g); z_g=cbind(1,z_g)

I have a variable that looks like this: > print(pred$posterior) o x 1 2.356964e-03 9.976430e-01 2 8.988153e-01 1.011847e-01 3 9.466137e-01 5.338627e-02 4 2.731429e-11 1.000000e+00 Now what I want to do is to access "o" and "x" How come this approach fail? > print(pred$posterior$o) or >