A common measure of multicollinearity is the Variance Inflation Factor (VIF). As the name suggests, the VIF is the factor by which the variance of a parameter inflates due to collinearity with other variable parameters. The VIF of the i th parameter may be computed empirically by measuring the variance over many simulated trials for two scenarios: (1) one in which all fitting parameters are varied during each fit (as is usual) and ( ) only the i th parameter is varied during the fit and all other fitting parameters are held constant at their true values. Then the VIF is the ratio of the former to the latter. However, the empirical method may be time consuming, so we elect for the equivalent theoretical expression: Column vectors of the Jacobian 𝐉𝐉 are normalized to 𝐉𝐉 ̂ such that all diagonal elements of 𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂ are equal to one. Then the VIF of the i th parameter is equal to the i th diagonal element of �𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂�-1 . To understand this, consider the limiting case of perfectly orthogonal model parameters and uniform, uncorrelated noise (i.e. 𝐕𝐕 𝐃𝐃 is the identity matrix), then 𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂ is the identity matrix, and hence, VIF is equal to 1 for every parameter. In the other limiting case in which any two or more parameters are perfectly linearly dependent, then 𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂ is rank deficient, causing det(𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂) = 0, and the diagonal of �𝐉𝐉 ̂𝐓𝐓𝐕𝐕 𝐃𝐃 -𝟏𝟏 𝐉𝐉 ̂�-1 blows up to infinity. For that reason, we recommend the true matrix inverse for testing VIF, not a pseudo inverse.