You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: spm_MDP_log_evidence.m
    path of file: ./repos/spm/toolbox/DEM
    
    the code of the file until where you have to start completion: function [F,sA,dFdA] = spm_MDP_log_evidence(qA,pA,rA)
% Bayesian model reduction for Dirichlet hyperparameters
% FORMAT [F,sA,dFdA] = spm_MDP_log_evidence(qA,pA,rA)
%
% qA  - sufficient statistics of posterior of full model
% pA  - sufficient statistics of prior of full model

    
function [F,sA,dFdA] = spm_MDP_log_evidence(qA,pA,rA)
% Bayesian model reduction for Dirichlet hyperparameters
% FORMAT [F,sA,dFdA] = spm_MDP_log_evidence(qA,pA,rA)
%
% qA  - sufficient statistics of posterior of full model
% pA  - sufficient statistics of prior of full model
% rA  - sufficient statistics of prior of reduced model
%
% F    - free energy or (negative) log evidence of reduced model
% sA   - sufficient statistics of reduced posterior
% dFdA - total (negative) free energy gradients with respect to rA
%
% This routine computes the negative log evidence of a reduced model of a
% categorical distribution parameterised in terms of Dirichlet
% hyperparameters (i.e., concentration parameters encoding probabilities).
% It uses Bayesian model reduction to evaluate the evidence for models with
% and without a particular parameter.
%
% A demonstration of the implicit pruning can be found at the end