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Team:NTNU Trondheim/Modeling
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(→Bayesian Hierarchy) |
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=== Bayesian Hierarchy === | === Bayesian Hierarchy === | ||
We then wish to model the reliability for the observations... That is the probability of false positive/negative results | We then wish to model the reliability for the observations... That is the probability of false positive/negative results | ||
| - | P(RTF = 1|stress) | + | P(RTF = 1|stress) |
and opposite. | and opposite. | ||
Having the observations from the lab | Having the observations from the lab | ||
| - | <b>x</b> = (x<sub>1</sub> , x<sub>2</sub> , · · · · , x<sub>n</sub>) | + | <b>x</b> = (x<sub>1</sub> , x<sub>2</sub> , · · · · , x<sub>n</sub>)<sup>T</sup> |
were x<sub>i</sub> is under condition C = 1 (stress) , and y<sub>j</sub> is under condition C = 0 (no stress). | were x<sub>i</sub> is under condition C = 1 (stress) , and y<sub>j</sub> is under condition C = 0 (no stress). | ||
Revision as of 09:16, 12 July 2011
Modeling
3 types of models: Systems of ODE, Bayesian hierarchy and linear classification problems (LDA or similar). To be continued....
Model Introduction
-What to model
-How to model
The Models
Systems of ODE
Bayesian Hierarchy
We then wish to model the reliability for the observations... That is the probability of false positive/negative results P(RTF = 1|stress) and opposite. Having the observations from the lab
x = (x1 , x2 , · · · · , xn)T
were xi is under condition C = 1 (stress) , and yj is under condition C = 0 (no stress).
Linear Classification
Non-linear Classification
Model Validation
References
