Team:NTNU Trondheim/Modeling

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(Difference between revisions)
Line 25: Line 25:
=== 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
-
<math>P(RTF = 1|stress)</math>
+
<math>P(RTF = 1|stress) ^{T}</math>
and opposite.
and opposite.
Having the observations  from the lab  
Having the observations  from the lab  
-
<math>\mathbf{x} = (x_{1} , x_{2} , \cdots , x_{n})</math>
+
<math>\mathtbf{x} = (x_{1} , x_{2} , \cdots , x_{n}) ^T</math>
were x_i is under condition C = 1 (stress) , and y_j is under condition C = 0 (no stress).
were x_i is under condition C = 1 (stress) , and y_j is under condition C = 0 (no stress).

Revision as of 07:52, 29 June 2011


Models are under construction -------Page

3 types of models: Systems of ODE, Bayesian hierarchy and linear classification problems (LDA or similar). To be continued....


Contents

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) ^{T} and opposite. Having the observations from the lab \mathtbf{x} = (x_{1} , x_{2} , \cdots , x_{n}) ^T were x_i is under condition C = 1 (stress) , and y_j is under condition C = 0 (no stress).

Linear Classification

Non-linear Classification

Model Validation

References

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