jags

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    JAGS error: Node inconsistent with parents

    I am trying to apply Benedict Escoto's method from the paper "Bayesian Claim Severity with Mixed Distributions," published in Variance. I seem to be running into a JAGS simulation problem. When I run the code, JAGS gives me the following error: Error in node ones[1] Node inconsistent with...
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    Comparing the output of JAGS to conjugate analysis - normal with unknown mean and var

    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...
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    Bayesian analysis of logistic dependent variable, with between and within subjects in

    I have a dataset consisting of two groups of participants (25 participants in one group and 26 in the other), with each participating in two conditions, and resulting a single binary outcome for each condition. I'm trying to analyze this model using JAGS, by combining some of Kruschke's...
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    Is it possible to make the analysis of rjags repeatable?

    Hi all, Thank you in advance for help. Usually, I set a seed in r script to make the result of analysis repeatable. However, if I use "rjags" package, the results are slightly difference every time I re-ran it even if the seed is set in R script. I guess I should do something in either the...
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    JAGS error for MCMC Bayesian inference

    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 ~...
  6. Lazar

    Cross-classification BUGS

    Hi All, I have a hierarchical Bayes model that looks like: model{ #Overaching model for (i in 1:n){ y[i] ~ dnorm (yHat[i], tau.y) yHat[i] <- a[id[i]] + b.group*group[i] + b.wave*wave[i] + b.int*wave[i]*group[i] } #Fixed Effects b.group ~ dnorm(0, .0001) b.wave ~...