SSMProblems.jl and GeneralisedFilters.jl provide a unified Julia interface for defining state space models and running Kalman, particle, and Rao-Blackwellised filter inference with GPU support.
The Geometry of Hamiltonian Monte Carlo
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abstract
With its systematic exploration of probability distributions, Hamiltonian Monte Carlo is a potent Markov Chain Monte Carlo technique; it is an approach, however, ultimately contingent on the choice of a suitable Hamiltonian function. By examining both the symplectic geometry underlying Hamiltonian dynamics and the requirements of Markov Chain Monte Carlo, we construct the general form of admissible Hamiltonians and propose a particular choice with potential application in Bayesian inference.
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State Space Model Programming in Turing.jl
SSMProblems.jl and GeneralisedFilters.jl provide a unified Julia interface for defining state space models and running Kalman, particle, and Rao-Blackwellised filter inference with GPU support.