Gibbs Random Field: Positive Probability Distribution Explained

In summary, the conversation discusses confusion about the statement that a probability distribution is positive and how it relates to Gibbs random fields. The participants also mention the possibility of negative probability distributions and question their existence. They also suggest looking into the work of M.S. Bartlett for more information on this topic.
  • #1
pamparana
128
0
Hello everyone,

I am trying to understand markov random fields and how it is related to the Gibbs measure and basically trying to understand the Gibbs-MRF equivalancy.

Anyway, while browsing Wikipedia documents, I was looking at the page on MRFs and when I came across the following line;

When the probability distribution is positive, it is also referred to as a Gibbs random field

I got confused with this. Aren't probabilities supposed to be positive. Why would be a probability distribution be negative? What does a negative probability distribution even mean? Would one use it in any possible case? So, are not ALL probability distributions gibbs random field?

Thanks,
/L
 
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  • #2
Hey pamparana.

I do recall hearing about this once before in the context of Dirac, but I never really gave it much thought, however the wiki page is probably a good place to start on learning this:

http://en.wikipedia.org/wiki/Negative_probability

The above says that a guy named M.S. Bartlett did the mathematical and logical consistency analysis of these kinds of distributions, so that would be a good place to start if you can't get something immediate on google.
 
  • #3
You've got me interesting in this, and a quick search came up with the following:

http://cs5824.userapi.com/u11728334/docs/8db4cf52c20c/Khrennikov_Interpretations_of_probability_34766.pdf
 
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  • #4
Wow, thanks! Crazy stuff!

Would need a lot of time to process this. In any case, it seems most of the probability distributions we encounter most of the time are Gibbs fields.

Many thanks for your replies.

Luc
 
  • #5
pamparana said:
Why would be a probability distribution be negative?

Instead of that, you shoud ask "Why would a probability distribution be zero?". (It could be zero at certain values.)

The statement in that article that the "the probability density is positive" doesn't imply that probability distributions can be negative. Look up clearer articles about Markov and Gibbs random fields. (There are various alternative theories of probability, but they are irrelevant to the usual treatment of Markov random fields.)
 

FAQ: Gibbs Random Field: Positive Probability Distribution Explained

What is a Gibbs Random Field?

A Gibbs Random Field is a statistical model used to represent the spatial relationships between random variables in a system. It is a type of Markov random field that assigns probabilities to the variables based on their neighboring variables.

How does a Gibbs Random Field work?

A Gibbs Random Field works by calculating the probability of a configuration of random variables based on the values of its neighboring variables. This is done using a conditional probability distribution, which takes into account the influence of neighboring variables on each other.

What is the significance of a positive probability distribution in a Gibbs Random Field?

A positive probability distribution in a Gibbs Random Field means that all probabilities assigned to the variables are greater than or equal to zero. This is important because it ensures that the model is mathematically sound and can accurately represent the relationships between the variables in the system.

How is a Gibbs Random Field different from other statistical models?

A Gibbs Random Field differs from other statistical models in that it takes into account the spatial relationships between variables, rather than just their individual values. This makes it particularly useful for analyzing data that has a spatial or network structure.

What are some applications of a Gibbs Random Field?

A Gibbs Random Field can be applied in various fields such as image processing, computer vision, and spatial statistics. It can also be used in social network analysis, gene mapping, and climate modeling. Essentially, it can be applied in any situation where there is a need to model the spatial relationships between random variables.

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