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I love how lightweight and interactive this is.
Related: Norvig's runnable intro probability notebooks at https://github.com/norvig/pytudes#pytudes-index-of-jupyter-i...
I haven't completed or deployed it yet but I wrote a C# API to calculate your chance of winning a hand of poker based on all known cards at the table.
All unknown cards are randomly assigned and it just loops a bunch, reasonably easy to implement and actually is reasonably fast.
https://github.com/JohnFarrellDev/PokerMonteCarloAPI/
With the amount of possible outcomes Bayesian statistics just didn't seem reasonable to implement.
Goes without saying this tool is still fairly basic, it shouldn't be used to inform how much to bet or when to fold as it doesn't take into account information such as how much your opponents are betting.
It was actually invented for this.
Open source radiation transport Monte Carlo code here if you'd like to play around:
https://github.com/openmc-dev/openmc
I wrote a Haskell version that includes two components:
A very efficient function to rank a set of Texas Hold’em hands.
A Monte Carlo situation that gives you the probability of winning each hand from any known amount of information.
It is available here: https://github.com/ghais/poker
I use GTO+ which is proprietary.
I just tried this one which works like a charm (just run the exe from the zip in github releases ; even comes pre-loaded with a wide amount of preflop ranges, which seem to come from a previous solve) : https://github.com/bupticybee/TexasSolver
Searching with "poker solver haskell" only seem to show very immature projects.