mcmc

Hi everyone I have e little dataset of biological data (concentrations) collected over a geographic range of 1000 km grouped in 4 spots (ca. 100 km each). Given the tiny sample (2*25 samples corresponding to two different time periods), I thought about Bayesian methods and a MCMC approach in...

Hi everyone... I'm new to Bayesian statistics and I'm trying to perform a BANOVA. I'm using the following book to help me: "Doing Bayesian Data Analysis A Tutorial Introduction with R and BUGS", written by John K. Kruschke. I'm working on a simulated data set, trying to find a difference in...

When using Bayesian MCMC in AMOS, do the MCMC algorithms allow for nonlinearity? I know all the other estimation algorithms do not [e.g. maximum-likelihood (ML)]. Also, does anyone know of any research articles that involve Bayesian CFA and measurement invariance that is similar or the same...

Dear Bayesians. I'm starting my way in the Bayesian world, and I'm trying to build a simple model for estimating the mean and variance of a normal distribution. I assume that: y=rnorm(100,50,4) # This would be the data mu0=0 #Prior of mean var0=100 #Prior for the variance #I continue...

Hi. Is there some good hands-on tutorial about running MCMC analysis for the meta-analysis of diagnostic tests? Is it inbuilt STATA Bayesian analysis is sufficient for this task or is necessary to install some additional patch? Thanks!

Dear all, I stumpled upon some really good answers in this forum so I decided to give it a try and hope that I can also enrich the ongoing discussions here. I try to implement a multivariate stochastic volatility model in Winbugs. Hereby I use the matrix Sigma.epsilon with stochastic parts...

In R, I am running an MCMC Bayesian inference for data from mixture of Gamma distributions. JAGS is used here. The model file gmd.bug is as follows model { for (i in 1:N) { y[i] ~ dsum(p*one, (1-p)*two) } one ~ dgamma(alpha1, beta1) two ~ dgamma(alpha2, beta2) alpha1 ~ dunif(0, 10) beta1 ~...

Hi Everyone, Problem: I have just completed a MCMC run and I have a model for a simple linear regression. I would like to get a distribution around a certain X value using the model. I know that for each step of the chain I need to generate simulated data using the model parameters at that...

I am trying to find a reference and/or guidance for fitting a mixture model to a population of data within an MCMC framwork where the global mean is constrained to a particular value, in this case 1. That is, E(X) = 1 where f(X) = \sum \omega_i f_i(x| \theta_i) and the standard constraint \sum...

Here is my situation. I am trying to predict the 'entire' distribution of the dependent variable, not just the mean( or conditional mean). Does it then make sense to seprateley predict quantiles of this variable to learn about the new predicted CDF? I intend to use this CDF as one input (say...

I've read a lot about how sequential importance sampling isn't useful in high dimensions, but not what is considered high. The reason for me asking is that I'm interested in fitting a state space model with 50 dimensions and 60 time points, but I'm not sure how useful sequential importance...

I have 4 random variables, say A,B,C and D and some data X. I can obtain the conditional pdfs P(A|B,C,D,X),..., P(D|A,B,C,X) up to proportionality. I would like to sample from the joint pdf P(A,B,C,D|X). This can easily be done via metropolis hastings. However I wish to also have the...

Hello everyone, Over the past few weeks I have been studying MCMC and Metropolis-Hastings algorithms. Unfortunately I am stuck and confused with the different variations of the algorithm when I try to practically implement it with code. I was looking for some code examples online but the more I...

I have a small problem with "impute missing values": I found it hard to find the full algorithm of spss to this method. If the numeral form of my data is: age, education, own apartment/not, salary etc. There are 50000 rows of information and about 5% of them are missing arbitrary. Fully...

Hi all, I have been trying to implement my own Gibbs sampler for a non defined posterior and am having trouble figuring out how to implement it. Since I have no formal training in MCMC (alas in my grad-school MLE ruled) I am also not certain if a Gibbs sampler is the best method to implement...