# 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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### Sampling from conditional distribution in general case

I'm dealing with Gibbs Sampling now. Let's consider the example: I know the distribution of X|Y and the distribution of Y. They are some known - Binomial or Beta or other but particular. Thus I have ...
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### Prior of multivariate Polya distribution?

Anyone knows a prior (preferably conjugate) to the multivariate Polya distribution? I need it for Gibbs sampling. So if anyone has another idea, I am interested.
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### Sampling posterior of empty cluster in GMM and Gibbs

Consider performing inference via a standard Gibbs sampler for a standard Gaussian Mixture Model (GMM) with $k$ components that are Gaussians $$\mathcal{N}(\mu_{k}, \sigma^{2}_{k})$$ where we assume ...
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### Blocked Gibbs Sampling using Forward / Backward Algorithm

I am new to machine learning and have been reading about gibbs sampling. From my understanding, a Gibbs algorithm samples a single variable iteratively conditioned on all other variables. In blocked ...
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### Gibbs Sampler - Sample mean convergence

To simulate from the posterior distribution $p(\theta|Y)$ where $\theta = (\mu,\lambda_1,\lambda_2)$, I run a Gibbs sampler to draw approximately random values from $p(\theta|Y)$. This Gibbs sampler ...
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### Need help deriving a gibbs sampler for a normal mixture model with two components

Let $\theta_i$ be an indicator that the i-th eruption is a long eruption. (i.e. $\theta_i = 1$ if the i-th eruption is long and $\theta_i = 0$ otherwise.) Assume the following model and derive a Gibbs ...
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### antagonistic simulated annealing

Simulated annealing aims at a series of target distributions $$\pi_T(x)\propto\exp\{T\,H(x)\}$$ to find the maximum of the function $H$ and its argument $$\arg_x\max_{x\in \mathfrak X} H(x)$$ if the ...
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### Gibbs Sampling vs. Using Raw Probability in Contrastive Divergence

In Hinton's Practical Guide to Training Restricted Boltzmann Machines, Section 3, he discusses different situations in which one should take a sample from the Gibbs sampling process, and other ...
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### Is burn-in necessary for MCMC/Gibbs sampling if I have samples from the true distribution already?

Say I have some samples from a distribution $p$, and I want to get more samples using MCMC/Gibbs sampling. Since the existing samples are known from the equilibrium distribution $p$, if I use them as ...
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### Convergence of approximate Gibbs sampling

We have a bivariate random variable $(X,Y)$ for which sampling is challenging. If we were to know how to sample from the conditionals $(X|Y)$ and $(Y|X)$, we could get samples from the joint using ...
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### Gibbs sampling for parameter estimation

I am reading the paper by Willemsen et al (2015), "A multivariate Bayesian model for embryonic growth", Statistics in Medicine, 34:8, 1351–1365 where they define the posterior distribution as, \begin{...
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### Bayesian estimation of Dynamic Regression with AR(1) parameters

I would like to draw (Bayesian) inference in a dynamic linear regression with regression parameters following independent AR(1) processes $\beta_{t,i} = \mu_i+\beta_{t-1,i}+w_{t,i}$. However, I ...
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### When Gibbs Sampling is fast/slow to converge?

Are there any heuristic/theories showing that on what kind of Bayesian models the convergence of Gibbs sampling is fast/slow? For example, from my limited experience, I feel (may be wrong) when a ...
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### Criteria in determining “step size” of Metropolis-hasting algorithms

I am training a complex Bayesian model using Gibbs sampling and Metropolis-Hasting algorithm. Most of the parameters are directly sampled by using conjugate priors except for 3 params which are ...
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### When 2% of the Bayesian Model have not converged?

I have model with 20000 latent parameters, set up in a Gibb's sampler. 98% of the parameters and sometimes 99.5% of the parameters satisfy the Geweke convergence statistic, have low autocorrelation ...
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### Derive Marginal Posterior to set up Gibbs-Sampler

I am currently trying to replicate a Hierarchical Model for multivariate returns proposed in the paper Portfolio selection using hierarchical Bayesian analysis and MCMC methods. However, in order to ...
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### Efficiency in Metropolis Vs Gibbs sampling

I have read that Gibbs sampling is more efficient than Metropolis algorithm. Why? Is this due only to the fact the in Gibbs sampling the acceptance rate is $1$, so that the chain needs fewer ...
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### Metropolis-Hastings Algorithm within Gibbs Sampling

I have this $f$ function below. $$f(x_1,x_2)\propto \left(\dfrac{x_1}{x_2}\right)\left(\dfrac{\alpha}{x_2}\right)^{x_1-1}exp\left\{-\left(\dfrac{\alpha}{x_2}\right)^{x_1} \right\}I_{R^+}(x)$$ where ...
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### Gibbs sampler implementation

I am just getting started with the Gibbs Sampler and came across an implementation from here and here and here. All of theses implementations are based on the first article. There is an inner loop ...
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### Gibbs sampling on the use of gamma distribution

I am trying to reproduce a Gibbs sampling but some points are unclear to me. Let's assume a linear model of the following form $$y=x\beta + \epsilon$$ The prior distribution for the beta ...
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### How does Gibbs sampling produce values for a variable using the univariate conditional probability?

I have a question about Gibbs sampling for generating samples. The Gibbs sampling algorithm is often stated. $x^0 = (x_1^0, x_2^0, \ldots, x_n^0)$ //initialize random values for $t=1$ in $T$ //...
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### Conditional distribution for Gibbs sampling for Gaussian mixture

If we draw $n$ i.i.d. points $x_1,x_2,\dots,x_n$ from the following Gaussian mixture: $$\frac 12 \mathcal N(x \mid \mu_1,1) + \frac 12 \mathcal N(x\mid \mu_2,1)$$ and the prior $p(\mu_1 , \mu_2 )$ ...
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### What is the difference between monte carlo integration and gibbs sampling?

I am aware that both are methods of sampling from the posterior. MC integration replaces the integral by a sample MC sample. Is this sample independent? Gibbs sampling is a class of MCMC ...
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### Why can Gibbs sampling outputs be used in Rao-Blackwellization?

I'm currently learning Chib (1995)'s method to calculate the marginal likelihood of a Bayesian model using Gibbs sampling outputs. I'm stuck in the Rao-Blackwellization step. Suppose $\mu$ and $\phi$...
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### Gibbs sampling from conditional full posterior distribution

I am reading the paper by Willemsen et al (2015), "A multivariate Bayesian model for embryonic growth", Statistics in Medicine, 34:8, 1351–1365 where they define the posterior distribution as, \begin{...
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### Is Coordinate Ascent algorithm related to Gibbs Sampling in some way?

I wonder if only me feel there are certain connections between them, I googled it for a long time, but found no where mentioned these two method. But to me, they indeed looked so related, Could anyone ...
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### Implementing Gibbs sampler in R from posterior distribution

I am referencing a follow-up idea from something I posted earlier (Zero-inflated Poisson and Gibbs sampling, proofs and sampling). I want to implement the Gibbs sampler, by generating a large (...
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### Gibbs sampling from posterior distribution using R

New to MCMC. I have a model, saying $$Y_i=\beta_0+\beta_1x_{i1}+\beta_2x_{i2}+\frac{e_i}{\sqrt{\mu}}$$ where $x_{ij}$ are fixed covariates, $e_i\sim N(0,1)$, $\beta_0$, $\beta_1$, $\beta_2$ and $\mu$ ...
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### Drawing from a conditional density

I have a simple question. Suppose $X=(X_1,X_2,X_3)$ is multivariate normal. What's the best (quickest) way to draw from the conditional density $X_1\mid \exp(X_1)+\exp(X_2)+X_3$?
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### What's the role of the data in Gibbs Sampling?

Trying to wrap my mind around Gibbs Sampling. Across many answers in this same forum, I constantly notice that the examples shown do not actually require an observed data set (First example (with R ...
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### Gibbs sampling and mixed distribution

For a project, I need to simulate from a joint distribution with both continuous and discrete variables that are dependent. The conditional distribution of any variable given the rest is known. I ...
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### Efficiently sampling from mixture distribution posterior

I have the following model:  \begin{align} \pi_1\sim & \text{Unif}(0,1)\\ \lambda_1,\lambda_2\sim & \text{Ga}(1,1)\\ z_i\sim & \pi_1^{1(z_i=1)}\pi_2^{1(z_i=2)}\\ p(y_i|\lambda_1,\...
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### Generating samples from Gibbs method

I have a following homework in a subject called "Monte-Carlo Methods". I would be very thankful, if you could help me with this one, because I'm a bit stuck with this one .. The task is as follows:...
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### Why does $P(\theta_1\mid D, \theta_2) \propto P(D \mid \theta_1, \theta_2)P(\theta_1)$ hold?

Suppose that in a Bayesian framework we have observed data $D$, using independent prior distributions on the parameters of the model, denoted by $\theta_1, \theta_2$. Then, the joint posterior ...
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### Does Gibbs sampling MCMC place limitations on the posterior?

I'm starting to learn about Gibbs sampling, having so far only worked with Metropolis-Hastings MCMC, and there's something I haven't grasped yet about the way Gibbs sampling works. To frame the ...
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### MCMC using GIBBS sampling: can different burn-in be used for different parameters?

I have run a stochastic volatility model with 4 parameters. I have used the Heidelberg and Welch convergence diagnostic. The result shows 3 out of 4 parameters have passed the stationary and half-...