Questions tagged [gibbs]

The Gibbs sampler is a simple form of Markov Chain Monte Carlo simulation, widely used in Bayesian statistics, based on sampling from full conditional distributions for each variable or group of variables. The name comes from the method being first used on Gibbs random fields modeling of images by Geman and Geman (1984).

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Inferring GMM parameters with Gibbs Sampling

On my book, "Machine Learning A Probabilistic Approach". It's stated that is straightforward to derive a Gibbs sampling algorithm to fit a mixture model, especially if we use conjugate priors. So ...
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How to create a distribution and sample?

Suppose we are given some small set of data on bundles of electrical wires and increasing voltages run through them, and we note how many of the individual wires fail. So for example, a large data ...
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Rewrite conditional formula with three variables using Bayes formula

In equation (5) on page 3 on this paper a conditional probability is rewritten using Bayes' formula. I started using this answer Can I rewrite conditional probability of three variables like this? ...
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Gibbs sampler from from $p(x) = C g(x)$ with $C$ unknown and discrete elements in $X$

By using the Hasting-Metropolis method, is there a way to draw samples from a distribution of this form: $$p(\textbf{x}) = C g(\textbf{x})$$ For $x$ being two dimensional and discrete. The reason that ...
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Eq. 45-46 in Gibbs Sampling for the Uninitiated

I am trying to figure out how Eq. 45 simplifies to Eq. 46 in the paper - "Gibbs Sampling for the Uninitiated" by Resnik and Hardisty.www.cs.umd.edu/~hardisty/papers/gsfu.pdf (page 15) Eq. 45 $$ \...
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How does this Sampler work for the Concentration parameter of Dirichlet Process?

I am puzzled by how this Gibbs sampler on section 6 of Escobar & West (1995) works. To put it in simple words, the aim is to sample $\alpha$. The defined terms are: $$\eta\sim \texttt{Beta}(a,b)$$ ...
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How does GibbsLDA++ ensure that we are sampling from a good posterior?

This is an extending of this question, which asked that whether we should do some estimating to ensure that we are really using a likely topic assignment instead of the one happened with low ...
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Uniform sampling of constrained binary vectors by Gibbs sampling

General statement of the problem: Let $x,y$ be two binary vectors, connected by the following constrains: $$y=f(x),\qquad x=g(y)$$ That is, $x$ determines $y$, and $y$ determines $x$. There are many ...
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Is it possible for Metropolis sampling to converge to the wrong value?

I have simulated data under three parameters of interest, say a, b, c. The prior I put on c was a Gamma, so it only takes positive values. The full conditionals of a and b are known distributions, but ...
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Gibbs sampling: ancillary and sufficient parametrization

After asking a question about Gibbs sampling earlier, I have another one for you. I have not been able to find laymen's background on this, the only referenced use I've found for this is in ...
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How does Shuffled-Complex-Evolution-Metropolis algorithm compare to other adaptive samplers (e.g. NUTS)?

I recently heard of the Shuffled-Complex-Evolution-Metropolis algorithm and am curious how it compares to other adaptive MCMC sampling algorithms. Unfortunately I am still learning about optimizing ...
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What sort of data would be appropriate to analyze under an MCMC method?

MCMC methods describe stochastic sampling but I'm not entirely sure the contexts in real datasets one would wish to apply MCMC methods. What kind of data could I gain insight into with MCMC methods?
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Gibbs within Metropolis

Consider a model with two parameters, $\alpha$ and $\beta$. We want to sample these two parameters conditioning on two data points, $d_1$ and $d_2$. Is it possible to use an algorithm like this: 1) ...
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MCMC for Bayesian Inference (Gibbs Sampling) Varying Observed Data

At every step $k$, a Markov chain Monte Carlo algorithm for Bayesian inference with Gibbs sampling draws a parameter of the model to fit, $\beta_i^{(k)}$, from the conditional $Pr\left(\beta_i^{(k)}|\...
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Proper likelihood function in acceptance probability of Gibbs Sampler

I have a question about the acceptance ratio used when implementing a random walk M-H in a gibbs sampler to generate sample paths of an unobservable process. When computing the likelihood of a set of ...
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How can I sample from the conditional distribution?

I am learning Gibbs Sampling, in which there is a step named sampling from conditional distributions. I don't understand: 1. where is the conditional distribution from? From a general case, how can I ...
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How to sample via blocked Gibbs the conjugate normal model?

i am trying to sample the two dimensional parameter $\theta=(\mu,\sigma^2)$, for the Normal model. I have the full conditionals being for the mean: $\pi(\mu_{j}|\ldots) \sim N\left(\frac{\xi_{j}\...
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Test for convergence within Gibbs sampler

I am running a Gibbs sampler for Multivariate Normal times Inverse Wishart posterior distribution with missing data imputation step. I am trying to check if my step of simulating covariance matrices ...