Fast and Generic Hidden Markov Models in Julia

Explore Hidden Markov Models (HMMs) in this JuliaCon 2024 conference talk that delves into the versatile HiddenMarkovModels.jl package. Learn how HMMs function with their hidden state and visible observation sequences, and discover their applications across bioinformatics, speech processing, and industrial maintenance. Move beyond traditional Gaussian HMMs to understand how to implement custom observation distributions, utilize arbitrary number types for enhanced precision, handle sparse transition matrices, compute loglikelihood gradients, and incorporate external control variables into dynamic systems. Examine the architectural principles behind HiddenMarkovModels.jl, seeing how each abstraction layer enables new capabilities while maintaining computational efficiency. Gain insights into one of the fastest and most adaptable HMM implementations available, suitable for both industrial applications and academic research. Access comprehensive resources including the package repository, documentation, tutorials, and presentation materials to further enhance your understanding of this powerful statistical modeling tool.