But to be honest I did very few of the exercises, from chapter 4 and onward most of the stuff Jayne says are “over complicated” in the sense that he derives some fancy function, but that is actually just the poison likelihood or whatever, so as long as you can follow the math sufficiently to get a feel for what the text says, then you can enjoy that all of statistics is derivable from his axioms, but you don’t have to be able to derive it yourself, and if you ever want to do actual Bayesian statistics, then HMC is how you get a ‘real’ posterior, and all the math you need is simply an intuition for the geometry of the MCMC sampler so you can prevent it from diverging, and that has nothing to do with Jaynes and everything to do with the the leapfrogging part of the Hamiltonian and how that screws up the proposal part of the metropolis algorithm.
This might help you https://github.com/MaksimIM/JaynesProbabilityTheory
But to be honest I did very few of the exercises, from chapter 4 and onward most of the stuff Jayne says are “over complicated” in the sense that he derives some fancy function, but that is actually just the poison likelihood or whatever, so as long as you can follow the math sufficiently to get a feel for what the text says, then you can enjoy that all of statistics is derivable from his axioms, but you don’t have to be able to derive it yourself, and if you ever want to do actual Bayesian statistics, then HMC is how you get a ‘real’ posterior, and all the math you need is simply an intuition for the geometry of the MCMC sampler so you can prevent it from diverging, and that has nothing to do with Jaynes and everything to do with the the leapfrogging part of the Hamiltonian and how that screws up the proposal part of the metropolis algorithm.