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how to calculate the log likelihood. For the gaussian Gamma and inversegaussian families it assumed that the dispersion of the GLM is estimated has been counted as a parameter in the AIC value and for all other families it is assumed that the dispersion is known. The exponentials in the probability density function is made more manageable and easily optimizable.
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And the negative log-likelihood as Li logpyi L i l o g p y i Now recall that when performing backpropagation the first thing we have to do is to compute how the loss changes with respect to the output of the network. Why the log is taken. L0 -3063145 log-likelihood for the null model L1 -2989266 log-likelihood for the alternative model c0 0942 scaling correction factor for the null model c1 0985 scaling correction factor for the alternative model p0 10 number of parameters estimated in the null model p1 12 number of parameters estimated in the alternative model.
1 -- Generate random numbers from a normal distribution.
Why the log is taken. This is absolutely fine because the natural logarithm is a monotonically increasing function. For a glm fit the family does not have to specify how to calculate the log-likelihood so this is based on the familys aic function to compute the AIC. The calculation for the expected values takes account of the size of the two corpora so we do not need to normalize the figures before applying the formula.
