Template:Bayesian test design: Difference between revisions
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== | == Bayesian Nonparameteric Test Design == | ||
The regular nonparametric analysis performed based on either the | The regular nonparametric analysis performed based on either the binomial or the chi-squared equation was performed with only the direct system test data. However, if prior information regarding system performance is available, it can be incorporated into a Bayesian nonparametric analysis. This subsection will demonstrate how to incorporate prior information about system reliability and also how to incorporate prior information from subsystem tests into system test design. | ||
===Assumption on System Reliability=== | === Assumption on System Reliability === | ||
If we assume the system reliablity follows a Beta | |||
If we assume the system reliablity follows a Beta distribution, the values of system reliability, ''R'', confidence level, ''CL'', number of units tested, ''n'', and number of failures, ''r, ''are related by the following equation: | |||
::<math>1-CL=\text{Beta}\left(R,\alpha,\beta\right)=\text{Beta}\left(R,n-r+\alpha_{0},r+\beta_{0}\right)</math> | ::<math>1-CL=\text{Beta}\left(R,\alpha,\beta\right)=\text{Beta}\left(R,n-r+\alpha_{0},r+\beta_{0}\right)</math> | ||
where Beta is the incomplete Beta function. | where Beta is the incomplete Beta function. If <span class="texhtml" sab="459">α<sub sab="460">0</sub></span> and <span class="texhtml" sab="461">β<sub sab="462">0</sub></span> are known, then any quantity of interest can be calculated using the remaining three. The next two examples demonstrate how to calculate <span class="texhtml" sab="463">α<sub sab="464">0</sub></span> and <span class="texhtml" sab="465">β<sub sab="466">0</sub></span> depending on the type of prior information available. | ||
{{btd w info on reliability}} | {{btd w info on reliability}} | ||
{{btd w info from subsystem tests}} | {{btd w info from subsystem tests}} | ||
Revision as of 16:18, 6 March 2012
Bayesian Nonparameteric Test Design
The regular nonparametric analysis performed based on either the binomial or the chi-squared equation was performed with only the direct system test data. However, if prior information regarding system performance is available, it can be incorporated into a Bayesian nonparametric analysis. This subsection will demonstrate how to incorporate prior information about system reliability and also how to incorporate prior information from subsystem tests into system test design.
Assumption on System Reliability
If we assume the system reliablity follows a Beta distribution, the values of system reliability, R, confidence level, CL, number of units tested, n, and number of failures, r, are related by the following equation:
- [math]\displaystyle{ 1-CL=\text{Beta}\left(R,\alpha,\beta\right)=\text{Beta}\left(R,n-r+\alpha_{0},r+\beta_{0}\right) }[/math]
where Beta is the incomplete Beta function. If α0 and β0 are known, then any quantity of interest can be calculated using the remaining three. The next two examples demonstrate how to calculate α0 and β0 depending on the type of prior information available.