Trouble is, they require that I provide a posterior log-likelihood function, which is a simple enough requirement that seems to be giving me problems for some reason.<BR><BR>I know it should be ...

In this example, the log likelihood function of the SSM is computed using prediction error decomposition. The annual real GNP series, y t, can be decomposed as where ...

Let be the maximum likelihood estimate (MLE) of a parameter vector and let be the log-likelihood function defined for parameter values . The profile-likelihood method reduces to a function of a single ...

The log likelihood is a weighted sum of neuronal responses, where the weight of each neuron is determined by the log of its own tuning function; for the case of motion, this is a cosinusoidal ...