21 November 2009

Ph. D. Thesis Update November 2009

In the last months my thesis work has been evolved drastically. So I found a lot of answers to the main questions that I was looking for and that caught the advance of my thesis work.

Well, now I know that the researcher instinct could not fail since I started with this thesis work in the 2007's summer I said "I need a statistical and probabilistic course" because my engineer formation only give me some hints about these topics. But unfortunately I didn't find a course like that in Cinvestav in my first Ph. D. phase then I tried to get in touch with a professor at Cinvestav that works in the stochastic systems area but I received an "encouraged" answer saying that I must go and search myself these knowledge in the books or literature.

The time passed and I focused in other areas as the Meta-analysis in medical area, theory of emergence behavior, model driven engineering, meta-models, etc. Then a good day in October I received an email from Dominique inviting me to take a course about Bayesian cognition in Grenoble and just I was reading a book that I found with my colleague Anissa in a "secret crate" about the probabilistic reasoning in multi-agent systems. I was taking the ideas from the book as a possible way to solve my thesis issues then I decided to take the course in Grenoble.

Each session was as if all the answers appeared as a slow dropping fall of water, they show me:
- Basic probabilistic notation and operations that are used in the Bayesian cognition area.
- How to treat in an uncertain enviroment (as the problem description phase in my thesis).
- Examples of solutions applied in the industry and scientific problems solved with it.
- How to create a statistical model that could be used in every solution that uses this approach.
- The different kind of induction algorithms to predict most accurate choices.

So, it was a very short course and I spend a lot of money going each week to Grenoble but I think that it was a very good investment. Now I've a clearest idea of how to solve and implement the problem description phase uncertainty issues and then how to manage them into the next phases.

Special thanks to Pr. Pierre B├Ęssiere and Pr. Julien Diard.