259 metropolis-hastings questions.

Metropolis sampling for Bayesian networks

Gibbs sampling is a profound and popular technique for creating samples of Bayesian networks (BNs). Metropolis sampling is another popular technique, though - in my opinion - a less accessible method. ...

Monte Carlo Metropolis: Standard Error and Acceptance

In a time series data generated by Monte Carlo Metropolis algorithm, when is the standard error (correlation between two data points is assumed to be negligible) is higher - when the change in the ...

13 When would one use Gibbs sampling instead of Metropolis-Hastings?

There are different kinds of MCMC algorithms: Metropolis-Hastings Gibbs Importance/rejection sampling (related). Why would one use Gibbs sampling instead of Metropolis-Hastings? I suspect there ...

7 Acceptance ratio in Metropolis–Hastings algorithm

2 answers, 10.444 views mcmc metropolis-hastings
In the Metropolis–Hastings algorithm for sampling a target distribution, let: $\pi_{i}$ be the target density at state $i$, $\pi_j$ be the target density at the proposed state $j$, $h_{ij}$ be the ...

13 Understanding Metropolis-Hastings with asymmetric proposal distribution

1 answers, 10.503 views mcmc metropolis-hastings
I have been trying to understand the Metropolis-Hastings algorithm in order to write a code for estimating the parameters of a model (i.e. $f(x)=a*x$). According to bibliography the Metropolis-...

Is the truncated normal distribution symmetric?

I am running a Metropolis-Hastings MCMC to find the distribution of a parameter that takes real, positive values. I was considering using the truncated normal distribution, and was wondering if I have ...

2 Metropolis Hastings algorithm without enough data [closed]

In a metropolis hastings algorithm if i have not data or enough data, this will give me the prior means? I am asking this because I have made an algorithm and when i use just a few data this is not ...

MCMC Metropolis - confused about prior distributions

I am studying the Metropolis-Hasting algorithm (from the book Understanding Computational Bayesian Statistics- Chap.6-7) in its two different formulations: Random Walk Candidate Density; Independent ...

5 Noninformative prior for variance, understanding and coding

I have three questions regarding the understanding behind and implementation of a noninformative prior for variance. I'm attempting to build a Metropolis sampler and I'm trying to sample from a ...

2 GARCH(2, 3) model with Metropolis-Hastings algorithm

0 answers, 20 views garch metropolis-hastings

6 MCMC in a frequentist setting

I have been trying to get a sense of the different problems in frequentist settings where MCMC is used. I am familiar that MCMC (or Monte Carlo) is used in fitting GLMMs and in maybe Monte Carlo EM ...

1 How would the size of my dataset influence MCMC output?

I'm runing MCMC using Metropolis-Hasting algorithm to fit an equation with 6 parameters on a dataset composed of 30 instances. How will the fact that my dataset is so small impact the posterio ...

2 Why does detailed balance not provide a stopping criterion in MCMC?

Like I undestand MCMC sampling, the fulfillment of the detailed balance equation guarantees that our MC has reached its stationary distribution (given we ensure ergodicity). Detailed Balance is: \$\...