I am running an estimation using Occbin and mode_compute = 6. Depending on the exact setup of the problem, I occasionally run into a hard error:

Error using chol
Matrix must be positive definite.
Error in gmhmaxlik_core (line 194)
dd = transpose(chol(CovJump));
Error in gmhmaxlik (line 100)
[PostMode, PostVariance, Scale, PostMean] = gmhmaxlik_core(fun, OldPostMode, bounds, gmhmaxlikOptions, Scale, flag, MeanPar, OldPostVariance, varargin{:});

That is, the minimiser makes it all the way to the â€ślast callâ€ť, and then fails due to the jumping matrix not being positive definite. Presumably, however, the matrix must have been positive definite while tuning the scale parameter and so on, so this seemed an unusual moment for an error.

It might be that this is the intended behaviour, but if so could I suggest adding some minimal error handling and a user-friendly error message (like the error messages when the initial likelihood is NaN)?

I do, but the runtime is extensive (~36 hours), at least on my computer. Happy to provide it if thatâ€™s reasonable for you, but I appreciate you obviously have other committments as well. An alternative is that I could run the process again with a Matlab breakpoint inserted just before the crash, and then save all the internals (M_, oo, and so on) so you donâ€™t need to start the process from scratch, if that would be more helpful.

Apologies for the delayed response. Here is the workspace (minus figures) at the breakpoint on line 194 of ghmaxlik_core.m. As before, the operation chol(CovJump) fails due to CovJump not being positive definite (and indeed it isnâ€™t, on inspection). I have left matlab frozen at the breakpoint just in case there is anything else I can provide you with (it is running on a remote server so this isnâ€™t a problem).

My working theory is that the output is too sparse - my model appears to be somewhat unstable and to have no Occbin solution at many parameter combinations which might be causing an issue in calculating the jumping matrix. I am running the model again on a seperate instance of matlab with more robust Occbin settings to see if this makes a difference.

EDIT: in answer to your second question, the console output was:

Initial value of the log posterior (or likelihood): -119266.4049
==========================================================
Change in the posterior covariance matrix = 5625.
Change in the posterior mean = 27.1992.
Current mode = 62614.9277
Mode improvement = 56651.4772
New value of jscale = 2.6446e-07
==========================================================
==========================================================
Change in the posterior covariance matrix = 1.2222e-06.
Change in the posterior mean = 27.2253.
Current mode = 115044.9376
Mode improvement = 52430.0099
New value of jscale = 3.4625e-14
==========================================================

Many thanks. Is there anything obvious I can do to make the jumps bigger? Or to put it another way, under what conditions does MC=6 converge on microscopic steps?