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	Having the observations  from the lab		Having the observations  from the lab
-	<math>\mathbf{x}=(x_{1},x_{2},\cdots,x _{n})^{T}<\math>	+	<math>\mathbf{x} = (x_{1} , x_{2} , \cdots , x_{n})</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).

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Revision as of 07:47, 29 June 2011

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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 1 Model Introduction 2 The Models 2.1 Systems of ODE 2.2 Linear Classification 2.2.1 Non-linear Classification 3 Model Validation 4 References

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 $\backslash mathbf\{x\}\; =\; (x\_\{1\}\; ,\; x\_\{2\}\; ,\; \backslash cdots\; ,\; x\_\{n\})$ 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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