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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Gibbs algorithm using negative binomial produces NAs

I have the following full conditionals distributions: $$ X_2|X_1=x_1\sim Bin(x_1,p)\\ X_1|X_2=x_2\sim NegBin(x_2,p) $$ So I'm using the following code to generate a sample from each one: ...
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Metropolis-within-Gibbs for parametric inference of a regressive model

I have a regressive model of this form \begin{equation} y=f(\theta)+\varepsilon \end{equation} to describe observations $y$, with noise $\varepsilon$ and a parametric function $f$ with parameters $\...
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Can Gibbs Sampling be used to generate synthetic data from the posterior distribution?

I am working with a few datasets. I would like to expand some of them somehow by creating synthetic data. Is it possible to use a Gibbs Sampler to, by sampling a given distribution of the original ...
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Does Gibbs sampling require to know the partition function?

As the title suggest, does gibbs sampling require to know the partition function? For example, if I want to sample variable $a$ and I have worked out $p(a|rest) \propto f(a|rest)$ where $rest$ ...
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Conjugate prior for DPGMMs using Gibbs sampling

I am using Gibbs sampling to infer DPGMMs. The prior for multivariate Gaussians is Normal-inverse Wishart. But it turns out that the covariances are not estimated accurately. Here is codes and results....
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How is the accuracy of the results of Gibbs sampler measured?

How is the accuracy of the results of Gibbs sampler measured? Most resources merely say to iterate it $k$ times. But how does one infer the accuracy of the result?
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FFBS algorithm for estimating mean log-return parameter in stochastic volatility jump model

I am currently attempting to replicate this model: https://arxiv.org/pdf/1809.01501.pdf in r. My (first) problem is regarding how to sample from conditional posterior for mu, $(μ_{(j)}|Y_n, J_{(j−1)}...
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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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Can someone explain Gibbs sampling in very simple words? [duplicate]

I'm doing some reading on topic modeling (with Latent Dirichlet Allocation) which makes use of Gibbs sampling. As a newbie in statistics―well, I know things like binomials, multinomials, priors, etc.―,...
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precision or variance of a Gamma distribution in a Gibbs Sampler?

I want to confirm my thinking on a quick question I have regarding the Normal-Gamma Gibbs sampler that we see so often, but I am unable to find a satisfactory answer. If we are interested in ...
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What is the correct way to write the elastic net?

I am confused about the correct way to write the elastic net. After reading some research papers there seems to be three forms 1) $\exp\{-\lambda_1|\beta_k|-\lambda_2\beta_k^2\}$ 2) $\exp\{-\frac{(\...
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177 views

What does it means of Normalization term of Gibbs distribution?

I am studying about Gibbs distribution concept and I am confusing a one term in that concept that is normalization term. According to the Hammersley–Clifford theorem, an random $x$ can equivalently be ...
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370 views

Posterior distributions of parameters in a AR(1) model

Consider a AR(1) model with states given by $x_t=\phi x_{t-1}+a_{t}$, $a_{t}\sim\mathcal{N}(0,\tau^2)$ and the observations given by $y_t=x_{t}+e_{t}$, $e_{t}\sim\mathcal{N}(0,\sigma^2)$ for $t=1,...
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Influence of word counts from DTM on LDA with Gibbs Sampling

I'm trying to wrap my head around Topic Modeling based on LDA with Gibbs sampling (Griffiths, Steyvers 2004: Finding Scientific Topics). What struck me when reading some Python implementations like ...
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Gibbs sampling for drawing samples and estimating parameters

I'm learning Bayesian inference by myself and having a difficulty for understanding Gibbs sampling. From what I understood, Gibbs sampling is to draw samples from a given probability distribution $p(...
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Gibbs sampler: how can thinning equal to the number of iterations work?

I fit an LDA topic model, using the R package topicmodels. No hiccups and everything runs smoothly, my question here is conceptual. When controlling the Gibbs sampler, the default value (in the ...
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446 views

Why Gibbs sampling?

I am new to Gibbs sampling and sampling in general, so here is a basic question. I am reading this tutorial. Equation (40) is our complicated joint probability and equation (49) the less complicated ...
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Direct/indirect sampling of conditionals in Gibbs sampling

I have some problems understanding the definition of Gibbs sampling. Let us take into consideration a bivariate distribution \begin{equation} \pi(x_1,x_2): S \subset \mathcal{R^2} \rightarrow \...
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278 views

Transition probabilities for Gibbs Sampling in a Markov Random Field

I am currently reading this paper on Restricted Boltzmann Machines. On page 22, Given a Markov Random Field $\mathbf{X} = (X_1,\ldots,X_N)$ w.r.t a graph $G = (V,E)$ where $V = \{1 \ldots N\}$ and $...
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An efficient way to generate multivariate normal distribution by Gibbs sampler? [closed]

When learning Gibbs sampler, the most used example is bivariate normal. But what if we want to simulate multivariate normal distribution? The computation (mean and variance) of conditional ...
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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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How to implement a M-H step in a Gibbs sampling

I am having trouble implementing a Metropolis Hastings step in a Gibbs sampling problem. The following code was taken from https://www.stat.colostate.edu/computationalstatistics/ Details: It is a ...
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Would an “importance Gibbs” sampling method work?

I suspect this is a fairly unusual and exploratory question, so please bear with me. I am wondering if one could apply the idea of importance sampling to Gibbs sampling. Here's what I mean: in Gibbs ...
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Conditional distribution of $\exp(-|x|-|y|-a \cdot |x-y|)$

I am trying to use Gibbs sampling or Metropolis-Hastings to draw samples from the joint distribution$$f(x,y)\propto\exp(-|x|-|y|-a \cdot |x-y|)$$ For this I need the conditional distributions of $x$ ...
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Gibbs sampling versus general MH-MCMC

I have just been doing some reading on Gibbs sampling and Metropolis Hastings algorithm and have a couple of questions. As I understand it, in the case of Gibbs sampling, if we have a large ...
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Why does sampling from the posterior predictive distribution $p(x_{new} \mid x_1, \ldots x_n)$ work without having to average out the integral?

In a Bayesian model, the posterior predictive distribution is usually written as: $$ p(x_{new} \mid x_1, \ldots x_n) = \int_{-\infty}^{\infty} p(x_{new}\mid \mu) \ p(\mu \mid x_1, \ldots x_n)d\mu $$ ...
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Gibbs sampler for a multilevel model with no predictors in R

I'm working on multilevel models and want to know how they are estimated in R. For that purpose I'm reading amongst other things "Data Analysis Using Regression and Multilevel/Hierarchical Models" by ...
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Gibbs sampling where I can only find the mode of conditionals?

I'm trying to solve a problem with Gibbs Sampling, so I'm trying to do: $$ x_1^1 \sim p(x_1 | x_2^0, x_3^0)\\ x_2^1 \sim p(x_2 | x_1^1, x_3^0)\\ x_3^1 \sim p(x_3 | x_1^1, x_2^1)\\ x_1^2 \sim p(x_1 | ...
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Finding the posterior distribution of mean and variance given data sample using Gibbs Sampling?

I have the following hierachical bayesian model - $\mathbf{x}|\mathbf{c},\sigma^2 \sim \mathcal{N}(\mathbf{x}|\mathbf{c},\sigma^2)$ $\mathbf{c}|\mathbf{c}_1,\sigma^2_2 \sim \mathcal{N}(\mathbf{c}|\...
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What will happen if we sample the most probable value in the Gibbs sampling?

I am now working with the Gibbs sampling. One problem that puzzled me is that when we use the Gibbs sampling, we always sample randomly from the conditional probability. What will happen if we sample ...
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Determine frequent states

I have a distribution from which I can sample (namely, a Boltzmann Machine). Which methods exists to determine frequent states (states with high probability) / the most frequent state (state with the ...
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forward sampling for Bayesian network with continuous variables and equation-based causal relationships

I have a physical system which can be represented by the following Bayesian network. It has the following characteristics 1) The encoded variables are continuous variables 2) The causal ...
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Convergence theorem for Gibbs sampling

The convergence theorem for Gibbs sampling states: Given a random vector $X$ with components $X_1,X_2,...X_K$ and the knowledge about the conditional distribution of $X_k$ we can find the actual ...
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Gibbs sampler for ARIMA AR(1) parameters: division by zero

Suppose the following AR(1) model: $$ y_t = \mu + \phi (y_{t-1} - \mu) + \epsilon_t $$ with $\epsilon_t \sim \mathcal{N}(0,\sigma^2)$. Following issue arises when sampling from $P(\mu_i \;|\; \phi_{...
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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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Gibbs sampling allocations for time dependent observations from this model

I observe $N$ observations $\{x_{1,t_1}, \dots, x_{N,t_N}\}$ from a $k$ component Gaussian Mixture model. The $i$th observation is seen at time stamp $t_i$ and is distributed such that each $x_{i,t_i}|...
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Metropolis-Hastings within Gibbs sampling

Suppose we have the following classical normal linear regression model: $$y_i = \beta_1 x_{1i} + \beta_2x_{2i} + \beta_3x_{3i} + e_i$$ where $e_{i} \sim iid.N(0, \sigma^2)$ for all $i = 1, 2, \cdots,...
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Gibbs Sampler for mixture models: shall I skip some samples to avoid to use correlated samples? [duplicate]

I am implementing a Gibbs sampler in order to estimate the parameters of a mixture model. Assuming that the parameters are contained in a vector $\theta$ what I will do is: Implement and run the ...
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bayesian decision making - comparing expected loss

The problem is like this: Suppose that I am considering which country should I invest on, country A and country B, based on their GDP growth rate $\alpha$. There are two possible choices for each ...
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Conditional distribution in this Gaussian Mixture Model

Say I observe $N$ observations $\{x_1, \dots, x_N\}$ from a $k$ component Gaussian Mixture model, with $k > 0$ known and is such that each $x_i|\boldsymbol{\pi}, \boldsymbol{\mu} \sim \sum_{j=1}^{k}...
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Gibbs sampler for Dirichlet Process concentration parameter

I am trying to implement a Gibbs sampler for Hierarchical Dirichlet process, but I cannot seem to correctly estimate the concentration parameters. I therefore started testing just this part of a ...
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1answer
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Conditional Distribution to sample

Suppose I have six data points (n,x): (14,5), (13,4), (7,3), (10,5), (12,7), (20,13) which are realizations of binomial experiments on n trials with x successes respectively.. And I assume I ...
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Is Gibbs sampling an MCMC method?

As far as I understand it, it is (at least, that is how Wikipedia defines it). But I've found this statement by Efron* (emphasis added): Markov chain Monte Carlo (MCMC) is the great success story ...
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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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How exactly does Gibbs sampling work in Markov Networks?

I was going through the Probabilistic Graphical Modelling course by Stanford and they used a network such as this one-https://imgur.com/gallery/k0C8FY2 Now if we want to sample P(A|B), how would we ...
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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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Sampling from partial posterior

I'm reading a paper where the authors have something like the following as one step in their MCMC: $$ \begin{align} y&=\rho_1 x_1+\rho_2 x_2+\epsilon\\ z&=\beta\tilde{y}+u \end{align} $$ ...
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How to find conditional distributions from joint

I want to learn about how to do Gibbs sampling, starting with finding conditional distributions given a joint distribution. While looking for examples, I found this blog post that I wanted to ...
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HDP: Gibbs sampler implementation

I am trying to recreate the model proposed by Gao et al. (2011), based on the Hierarchical Dirichlet Process proposed by Teh and al. (2005). To estimate the model (let's call it iHDP) I need to ...
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Can Bayesian Optimization solve this problem?

Suppose ${\bf{x}} = (x_1,\ldots,x_n)$ and $f({\bf{x}})\propto 1_A({\bf{x}}) \prod_{i=1}^n {x_i}^{\alpha_i-1} e^{-\beta_i x_i}$ , i.e. $f$ is proportional to the product of independent gamma ...