I need to build a complex probability model to describe some "real world" scenarios. The system consists of several types of objects, and the contraints upon these objects and their interactions are partially known. The goal is to determine the parameters of the model with the input of real world "training" data.
To this end, how can I put all these probability constraints all together to let them interect with each other in a good way?
For example, I want to model travelling behavior of a family:
- Ed is college student, son of Tim and Barbara. They have a Ford, a VW and a bike.
- Tim goes to work almost every working day.
- Tim likes driving the VW to work, but sometimes also travel with buses.
- Ed also likes driving the VW, but sometimes also ride to school.
- Ed goes to school not very often(60%).
- Barbara goes shopping regularly.
- Barbara will drive only when 2 cars are both left at home, and she likes the Ford.
- The cars will be more probable to malfunction if driven too often.
- In raining days, nobody like the bike.
10.Tim sometimes drives the Ford, if he has driven the VW for a long period of time.
Assume I have a probabilit model for each of the 10. And I have the data of who has driven what in a whole year.
Now How can I put all the 10 constraints together into a dynamic system and find the proper parameters after input the one-year data?
What I want to depict is a system with many sub parts of different types. Each part has its own regularity and the parts interect with each other in time domain.
What is the computer-friendly way? better the state-of-the-art.
Thanks a lot!