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The ParSA library documentation gives the following equation which can be used to estimate performance of an MIR run

The ParSA library documentation gives the following equation which can be used to estimate performance of an MIR run

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|<math> P(\chi_n \notin Cost_{min}) \sim \left(\frac{K}{n}\right)^{\alpha} </math>

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|(1)

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<math> P(\chi_n \notin Cost_{min}) \sim \left(\frac{K}{n}\right)^{\alpha} </math>

|align="center" width="80"|(1)

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where ''P'' is the probability of non-convergence, <math>\chi_n</math> is a solution of a run of length ''n'', <math>Cost_{min}</math> is the minimum acceptable solution, and ''K'' and <math>\alpha</math> are problem specific parameters.  ''K'' and <math>\alpha</math> can be determined by plotting the Bayesian estimator for ''P'' versus ''n'' on a log scale and determining the slope and y-intercept.  The expression for the Bayesian estimator <math> \hat{p} </math> is given by

where ''P'' is the probability of non-convergence, <math>\chi_n</math> is a solution of a run of length ''n'', <math>Cost_{min}</math> is the minimum acceptable solution, and ''K'' and <math>\alpha</math> are problem specific parameters.  ''K'' and <math>\alpha</math> can be determined by plotting the Bayesian estimator for ''P'' versus ''n'' on a log scale and determining the slope and y-intercept.  The expression for the Bayesian estimator <math> \hat{p} </math> is given by

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{|width="50%"

|<math> \hat{p} = \frac{n_f + 1}{N + 2} </math>

|align="right"|

|(2)

<math> \hat{p} = \frac{n_f + 1}{N + 2} </math>

|align="center" width="80"|(2)

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