jpkrooney
commented
3 years ago After some further thought and experimentation I'd suggest a clip value of 0.25 instead of 0.5. i.e.
sig_ml = sig_ml.clip( 0.25 ) # To prevent divide by zero in marginal_p function
The logic being as follows: the denominator for the calculation in marginal_p is:
(2. * sig) - 0.5 * np.log(2 * np.pi * sig)).transpose((1, 0, 2))
This output of this function reaches a minima at 0.25 as sig varies as shown in this graph:
Below sig = 0.25 the function quickly becomes asymptotic. In test runs with different values for the clip, 0.25 produce higher TCS than a clip of 0.25.
Proposed solution for issue #19 I have added the line:
sig_ml = sig_ml.clip(0.5) # To prevent divide by zero in marginal_p functionthe estimate_parameters function in order to later divide by zero issue in the marginal_p function. As discussed in issue #19 , 0.5 is a somewhat arbitrary figure, however it seems to work well in practice. However you may wish to explore this further before implementing this fix.