Debugging

Numerical underflow

The most frequent error you will encounter is an underflow during inference, caused by some values being infinite or NaN. This can happen for a variety of reasons, so here are a few leads worth investigating:

• Increase the duration of the sequence / the number of sequences to get more data
• Add a prior to your transition matrix / observation distributions to avoid degenerate behavior (like zero variance in a Gaussian or zero probability in a Bernoulli)
• Reduce the number of states to make every one of them useful
• Pick a better initialization to start closer to the supposed ground truth
• Use numerically stable number types (such as LogarithmicNumbers.jl) in strategic places, but beware: these numbers don't play nicely with Distributions.jl, so you may have to roll out your own Custom distributions.

Method errors

This might be caused by:

• forgetting to define methods for your custom type
• omitting control_seq or seq_ends in some places.

Check the API reference.

Performance

If your algorithms are too slow, you can leverage the existing Interfaces to improve the components of your model separately (first observation distributions, then fitting). The usual advice always applies:

• Use BenchmarkTools.jl to establish a baseline
• Use profiling to see where you spend most of your time
• Use JET.jl to track down type instabilities
• Use AllocCheck.jl to reduce allocations