... ... @@ -112,23 +112,11 @@ in a fashion which is compatible with our inference machinery called NIFTy. \end{itemize} \section*{Mathematical description of what NIFTy needs} The Bayesian inference will be a minimization problem in the end. NIFTy comes with a variety of minimizers in order to solve this problem. These minimizers differ in their order. If you want to use only first order minimizers, you need to provide the following two functions: The abstract definition of what you'll need to implement is very short: \begin{itemize} \item The log-likelihood $\mathcal H (d|s) := - \log \mathcal P (d|s)$ as a function of $s$. \item Its derivative $\mathcal H' = \frac{\partial}{\partial s} \mathcal H (d|s)$. \end{itemize} If you want to use second order minimizers (which is definitely worth it), another object is needed: \begin{itemize} \item $\langle \mathcal H' \mathcal H'^\dagger \rangle_{\mathcal P (d|s)}$. \end{itemize} Note that for Gaussian, Poissonian and Bernoulli likelihoods this term doesn't need to be calculated and implemented because NIFTy computes it automatically. That's it. The rest of this paper explains what these formulae mean and how to compute them. From now one, our discussion will become increasingly specific. ... ...