All Questions
Tagged with prior markov-chain-montecarlo
49
questions
1
vote
1
answer
44
views
BIC with non-negligible priors
I want to do model selection based on the best-fit/MAP/marginal posterior I find from an MCMC and likelihood maximization. I have a likelihood $\mathcal{L}(X|\theta)$, some informative priors $\pi(\...
- 13
1
vote
0
answers
14
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Importance sampling for a parameterized family of distributions using a wide distribution from the same family
I'm motivated here by a problem for robust Bayesian analysis. Let $l(Y|X)$ be the likelihood and let $\{p_\xi(X)\}$ be a parameterized family of prior distributions where $\xi$ denotes the ...
- 11
2
votes
2
answers
636
views
Parameterization of inverse gamma prior in Bayesian methods
For a prior of $\sigma^2 \sim IG(0.01, 0.01)$, often recommended as an uninformative prior for the variance parameter in MCMC approaches and other Bayesian methods, which parameterization does this ...
- 725
1
vote
1
answer
77
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Is it ok to widen a prior during an MCMC which did not converge yet?
I am calibrating parameters of a process model. The runtime of the model is high and the calibration already ran for more than two weeks with many cores on a HPC.
After almost 150k iterations I ...
- 21
1
vote
0
answers
107
views
Choosing between Gaussian/Laplacian prior distributions for MCMC regression
When doing a linear regression using MCMC, you have to specify prior distributions for the values of the regression coefficients of the independent variables. If all of the priors are Gaussian ...
- 173
0
votes
1
answer
255
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Application of spike and slab for sampling from posterior distribution (bernoulli and beta)
I think the gamma N term in the first equation relates to the spike and prior. However, I am unsure what the rhs of the first is used for? Further, I am unsure what the pie term of the second equation ...
- 103
2
votes
0
answers
175
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What are the bayesian prior distributions to use for a binomial model with unknown $n$ and $p$
I a experimenting with a new MCMC software and before I delve into more complicated models I wanted to run some simple simulations. This is a very very simple simulation, so not meant to be very ...
- 1,522
0
votes
0
answers
36
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Converting posteriors to likelihoods by removing prior
I have a set of MCMC chains (i.e., unnormalized posteriors) for a parameter I modeled for a sample of objects. I have a model that requires that I condition on the likelihoods of this parameter. My ...
- 101
0
votes
0
answers
73
views
How to parametrize a posterior to use it as a prior in Bayesian statistics?
In my problem, I have two sets of parameters, $\theta_1$ and $\theta_2$, and two datasets $d_1,d_2$ that constrain them with a known likelihood function. There is a certain 'hierarchy' in the model: ...
- 151
3
votes
1
answer
1k
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Priors and nested random effects in MCMCglmm?
I am trying to construct a zero inflation Poisson GLMM using MCMCglmm(). I am new to Bayesian Statistics and this function and I am struggling to understand a couple of things.
For my data I am ...
- 31
1
vote
1
answer
278
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For Prior definition in bayesian regression with R package MCMCglmm, how to convey different strength of believe via parameter nu?
I understand the strength of the Prior is set via parameter nu however, I can not find information what nu expresses in statistical terms, e.g. how strong would a prior that is similar to the number ...
0
votes
1
answer
191
views
Setting priors for bivariate regression
I would like to perform a bivariate MCMC regression with boldness scores as the continuous response variable, aggression ranks as the ordinal response variable, trial numbers as fixed effect and ...
- 57
0
votes
0
answers
24
views
Sampling a proposed value with a limited range target when running MCMC [duplicate]
I want to do an MCMC algorithm and need to sample a proposed value from a proposed distribution. In the Metropolis algorithm, people usually use a normal distribution as proposal. But if the prior ...
- 23
5
votes
1
answer
328
views
Why in Hamiltonian MCMC do we multiply the posterior distribution by the likelihood?
So maybe I am misunderstanding what the author is staying, but I am reading Chapter 14 of Kruschke's Doing Bayesian Analysis. I am reading about the software Stan and how it uses the Hamiltonian MCMC ...
- 3,273
2
votes
0
answers
144
views
Posterior with a much larger uncertainty than the prior [closed]
I have done an MCMC analysis with many variables. One of my nuisance parameters has a Normal prior distribution with mean 0 and standard deviation 1. The posterior distribution for this parameter has ...
- 3,152