# mrcImageVarianceMap

## Usage

    Usage: mrcImageVarianceMap
    Options:
        [-i[nput]            In                  (NULL      )] :Optional  :In: InputList
        [-I[nput]            In2                 (NULL      )In2Var              (NULL      )In2N                (0         )] :Optional  :In: Input
        [-Nor[malizingParameter]NorA                (0.0       )NorB                (1.0       )] :Optional  :In: A + B*x: NormalizingParameter
        [-r[ef]              Ref                 (NULL      )] :Optional  :Ref: InputList
        [-R[ef]              Ref2                (NULL      )Ref2Var             (NULL      )Ref2N               (0         )] :Optional  :Ref: Input
        [-Nor[malizingParameter]RNorARef             (0.0       )NorBRef             (1.0       )] :Optional  :In: A + B*x: NormalizingParameter for Ref
        [-I[and]R            InAndRef2           (NULL      )InAndRef2Var        (NULL      )InAndRef2N          (0         )] :Optional  :In and Ref: Input
        [-o[utput]           Out                 (NULL      )] :Optional  :Output: Variance Probability
        [-v[arianceMap]      OutV                (NULL      )] :Optional  :Output: Variance Map
        [-c[onfig]           configFile          (NULL      )] :Optional  :ConfigurationFile
        [-m[ode]             mode                (0         )] :Optional  :Mode
    ----- Additional Usage -----
    ---- Variance Map -----
    -- Algorithm
       VarianceMap := VarinceOfAll/((VarianceOfDataSet1*n1+VarianceOfDataSet2*n2)/n1+n2)
                      VarinceOfAll:      variance of all data from Set1 and Set2
                      VarinceOfDataSet1: variance of all data from Set1
                      VarinceOfDataSet2: variance of all data from Set2

       Kai-distribution: Kai^2 = SumOf (Xi - AvgOfXi)^2/sigma^2
                               = s^2*(n-1)/sigma^2  : degree of freedom n-1
                                 when variables Xi are due to N(u, sigma^2)
       Kai-distribution: Kai^2 = Kai1^2 + Kai2^2 : degree of freedom m1 + m2
       F-distribution: F = (Kai1^2/k1)/(Kai2^2/k2)           : degree of freedom : (k1, k2)
                           (s1^2/sigma1^2)/(s2^2/sigma2^2)   : degree of freedom : (k1-1, k2-1)
       If sigma1 == sigma2, then
                           s1^2/s2^2  (Variance Ratio)       : degree of freedom : (k1-1, k2-1)