Markov Chain Monte Carlo question

In summary, the conversation discusses finding a regular transition matrix that is not time reversible, and suggests using linearly dependent rows to satisfy the probability conditions. The conversation also mentions using the matrix's transpose and provides an example of a non-time reversible matrix.
  • #1
mjt042
9
0
I was wondering if anyone could help me with this problem dealing with Markov Chain Monte Carlo
-Find a regular transition matrix that is not time reversible, i.e., doesn't satisfy the
balance equations?
My understanding from Markov Chain Monte Carlo is that for the transition matrix to be regular the matrix has to have all positives entries and each row will add up to one. I was thinking the trick to this problem for it not satisfy the balance equation would be to take the transpose of the transition matrix. I was hoping someone could give me a hint if I am on the right track of thinking and where to go from there.

Thanks
 
Physics news on Phys.org
  • #2
Hey mjt042 and welcome to the forums.

When you mean time reversible do you mean going from transition matrix at state n+1 back to n?
 
  • Like
Likes 1 person
  • #3
Thanks and yes.
 
  • #4
Consider a matrix with linearly dependent rows (i.e. a determinant of zero) that still satisfy the probability conditions.

In this situation things are not time reversible since you can not solve for the inverse.
 
  • Like
Likes 1 person
  • #5
.4 .6
.6 .4 so the matrix to the left would work?
 
  • #6
No the determinant for this is non-zero.

Consider the matrix

.4 .6
.4 .6
 
  • #7
Thanks
 

Related to Markov Chain Monte Carlo question

1. What is a Markov Chain Monte Carlo (MCMC) algorithm?

A Markov Chain Monte Carlo (MCMC) algorithm is a computational technique used for sampling from complex probability distributions. It uses the principles of Markov chains to efficiently generate a sequence of random samples that approximate the desired distribution.

2. What is the purpose of using MCMC in scientific research?

MCMC algorithms are commonly used in scientific research to explore and analyze complex data sets and to estimate parameters of statistical models. They are particularly useful for problems that involve high-dimensional or multi-modal distributions, where traditional sampling methods may not be effective.

3. How does MCMC differ from other sampling methods?

MCMC differs from other sampling methods in that it uses a "memoryless" approach where each new sample only depends on the previous one. This allows for a more efficient exploration of the entire distribution, as opposed to just the local area around the current sample.

4. What are some common applications of MCMC in scientific research?

MCMC algorithms are commonly used in fields such as statistics, physics, biology, and machine learning. Some specific applications include Bayesian inference, parameter estimation, and model fitting in various scientific and engineering fields.

5. What are some challenges or limitations of using MCMC?

One limitation of using MCMC is that the quality of the results can be affected by the choice of starting point and the length of the sampling chain. Additionally, MCMC may not be suitable for problems with highly complex or ill-defined distributions. It also requires careful tuning of algorithm parameters, which can be time-consuming and labor-intensive.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
859
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
Replies
5
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
2K
Replies
67
Views
5K
Back
Top