It is not clear to me what aspect of the probabilistic analysis you are interested in. Are you looking for general references on the subject?

yes, please send me a reference that i can apply this analysis by matlab.

'Probabilistic analysis' is a rather wide definition of what you want to do. Why do you need to do it? What is your goal? What do you want to analyze? If you can clarify that it will be easier to come up with suitable references, because there are many of them and there is not a cookbook for it which you can just apply (actually, just applying, without considering the specific issues of your problem is a dangerous business).

I agree with Wouter. The subject covers huge range of topics, both in applied and theoretical subjects. In addition, there are also different trends and approaches to the same problem, depending on whom you ask. I am working with topics in applied physics and engineering. In my case, the Bayesian approach has proven to be the most useful. My recommendation is the little book by Sivia and Skilling, Data Analysis: A Bayesian Tutorial 2nd Edition. This book will put you on the right track. There are also a series of lectures on youtube by David MacKay on information theory, pattern recognition and neural networks which addresses the probabilistic modelling. Take a look at the lectures 10-13.

http://www.amazon.com/Data-Analysis-A-Bayesian-Tutorial/dp/0198568320

https://www.youtube.com/watch?v=mDVE0M-xQlc

I agree with the other colleagues, it is a question too broad. Anyway, you can get other references in the link.

Thesis Methodologies for risk assessment

The easiest thing to do is to use a software package including several distributions, so you could try many of them and have an idea on which ones relates better to your data. SPSS, STATISTICA, MATLAB and R are good choices. Any of them will allow you to perform a fitting procedure without having a deep knowledge on statistics.

Good Luck!

Hopefully, this article may give you some ideas:

https://towardsdatascience.com/managing-uncertainty-in-computational-science-and-engineering-5e532085512b