21 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 Bèssiere and Pr. Julien Diard.