Parameter Estimation - ML vs. MAP

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ParameterEstimationPeter NRobinsonEstimatingParametersfrom DataMaximumLikelihood(ML)EstimationBetad istributionMaximum aposteriori(MAP)EstimationMAQParameter EstimationML vs. MAPPeter N RobinsonDecember 14, 2012ParameterEstimationPeter NRobinsonEstimatingParametersfrom DataMaximumLikelihood(ML)EstimationBetad istributionMaximum aposteriori(MAP)EstimationMAQEstimating parameters from DataIn many situations in bioinformatics, we want to estimate op-timal parameters from data. In the examples we have seen inthe lectures on variant calling, these parameters might be theerror rate for reads, the proportion of a certain genotype, theproportion of nonreference bases etc.

Parameter Estimation Peter N Robinson Estimating Parameters from Data Maximum Likelihood (ML) Estimation Beta distribution Maximum a posteriori (MAP) Estimation MAQ ML estimate The ML estimate of the parameter is then argmax Xn i=1 [x ilog + (1 x )log(1 )] (8) We can calculate the argmax by setting the rst derivative equal to zero and solving for

  Parameters, Estimates, Estimation, Parameter estimation