Appendix 2: Regularised maximum likelihood
Let θ be a vector of parameters, let Y be the data and L(θ;Y) be the data likelihood function. Then the parameters of the model are estimated by maximising the likelihood function subject to a penalty function:
where 
The penalty function is a function of the distance between 
and the prior 
, and the initial variance 
of the parameter. We can put more weight on the prior by increasing the value of p.
