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Revision as of 09:48, 27 June 2011 (view source) Rahmilale (Talk | contribs) (→Systems of ODE) ← Older edit		Revision as of 07:44, 29 June 2011 (view source) Christpa (Talk | contribs) (→Systems of ODE) Newer edit →
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	<math>P(RTF = 1|stress)</math>		<math>P(RTF = 1|stress)</math>
	and opposite.		and opposite.
-	Having the observations	+	Having the observations  from the lab
-	x_i, i = 0,1,...,n, and y_j , j = 0,1,...,m,	+
		+	<math> \mathbf{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).

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Revision as of 07:44, 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

Linear Classification

Non-linear Classification

Model Validation

References

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