search for: posteriors

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

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,

With predict.lda the posterior probabilities only relate to the existing Class definitions. This is fine for Class definitions like gender but it is a problem when new data does not necessarily belong to an existing Class. Is there a classification method that gives posterior probabilities for Class membership and does not assume the new data must belong to one of the existing Classes? A new

This question is slightly off topic, but I'll use R to try and make it as relevant as possible. I'm working on a problem where I want to compare estimates from a posterior distribution with a uniform prior with those obtained from a frequentist approach. Under these conditions the estimates should agree. Specifically, I am asking the question, "What is the probability that the true

Hi all, I did some analysis using package R2WinBUGS to call WinBUGS. I set the iterations to 50000 (fairly a large number, I think), but after the program was done, the effective posterior samples contained only 7 draws. I don't know why. By the way, I checked posterior sample size by using bugsobj$n.sims. And, for my previous practice with WinBUGS/R2WinBUGS, no such strange thing happend.

Dear R useRs, i try to compute the posterior mean for the parameters omega and beta for the following posterior density. I have simulated data where i know that the true values of omega=12 and beta=0.01. With the function postMeanOmega and postMeanBeta i wanted to compute the mean values of omega and beta by numerical integration, but instead of omega=12 and beta=0.01 i get omega=11.49574 and

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

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

Hi all, I used both OpenBugs and R function bugs{R2WinBUGS} to run a linear mixed effects model based on the same data set and initial values. I got the same summary statistics but different posterior samples. However, if I order these two sets of samples, one is generated from OpenBugs and the other is generated from R, they turn to be the same. And the samples from R do not have any

Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

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 >

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)

Hello, I'm doing bayesian network analyses with the deal package. I am at a loss for how to interpret output from the analysis (i.e. what is a good score, what is a bad score, which stats tell me what about the network edges/nodes). Here is an example node with its posterior scores for all parent nodes. ------------------------------------------------------------ Conditional Posterior:

Dear R-users I am looking for a help in using the "deal" package. I followed the manual and paper from the author's web site to learn it, as shown below, but I could not figure out how to access the local and posterior probability of the nodes in the constructed network. library(deal) data(ksl) ksl.nw <- network(ksl) ksl.prior <- jointprior(ksl.nw) banlist <-

Dear R users, I'm trying to carry out monte carlo integration of a posterior density function which is the product of a normal and a gamma distribution. The problem I have is that the density function always returns 0. How can I solve this problem? Here is my code #generate data x1 <- runif(100, min = -10, max = 10) y <- 2 * x1^2 + rnorm(100) # # # # # # # # Model 0 # # # # # # #

Hi. I asked a question about lda() and got some answers. However, one question remains (which is not independent of the earlier ones): What output does lda() produce which I can use to compute the posteriors? I know predict(lda())$posterior will give me precisely the posteriors, but suppose I'd like to compute them myself, outside of R. So far, I have not been able to use "coefficients of linear discrimiants" to do this, for they don't seem to be the alpha and beta in log(post)...

IHi all, have a dataset with rows as plots and environmental data as columns. I have predicted the values using the following ed.pred<-predict(lda.ed,ed) #lda.ed the model, ed the env. variables used for the prediction plots I am wanting to know the formula used by predict.lda for calculating the posterior probabilities. Can anyone point me in the right direction? Thanks

Dear All, This is a question somewhat off-topic. Say, if I have known the number of components in the mixture, all the estimated parameters, prior probabilities, and so on for a finite mixture model, how might I compute the posterior probabilities of each case for a new dataset without observed response (Y)? I want to know the parametric form of such calculation such that I can calculate it

Dear R list, I have written some code to produce several wireframe plots in a panel. They look good, but when I try to create a pdf, many (but not all) of the details I have specified are not reproduced. For example, the line width I have specified is not reproduced, and neither are the font sizes for the axis labels. I'm an R novice, so I could really use some guidance. Here is the code I am