mle memory error with custom negative log-likelihood function

Your issue is line 228 (as of MATLAB R2017b) of the in-built mle function, which happens just before the custom function is called:

data = data(:);

The input variable data is converted to a column array without warning. This is typically done to ensure that all further calculations are robust to the orientation of the input vector.

However, this is causing you issues, because your custom function assumes data is a row vector, specifically this line:

out = sum(log(1+exp(lambda))-data.*lambda);

Due to implicit expansion, when the row vector lambda and the column vector data interact, you get a huge square matrix per your error message.

Adding these two lines to make it explicit that both are column vectors resolves the issue, avoids implicit expansion, and applies the calculation element-wise as you intended.

lambda = lambda(:);
data = data(:);

So your function becomes

function out = loglike(data, params, T_1, T_2, npast)
    N = length(data);
    if npast == 0
        past = 0;
        past = zeros(1,N);
        past(npast+1:end) = movmean(data(npast:end-1),[npast-1, 0]); % Average number of events in the previous n years
    lambda = params(1) + ...
        (params(2)*cos(2*pi*(1:N)/T_1)) + ...
        (params(3)*sin(2*pi*(1:N)/T_1)) + ...
        (params(4)*cos(2*pi*(1:N)/T_2)) + ...
        (params(5)*sin(2*pi*(1:N)/T_2)) + ...
    lambda = lambda(:);
    data = data(:);
    out = sum(log(1+exp(lambda))-data.*lambda);

An alternative would be to re-write your function so that it uses column vectors, but you create new row vectors with the (1:N) steps and the concatenation within the movmean. The suggested approach is arguably “lazier”, but also robust to row or column inputs.

Note also I’ve changed your variable name from size to N, since size is an in-built function which you should avoid shadowing.

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