HMMs with Ordered Hidden States and Emission Densities
Extract Model Estimates
Calculate and Visualise a Confusion Matrix
Converts MCMC Samples into ggmcmc Format
Calculate Eigenvalues and Eigenvectors
Example of a Simulated Gamma-Poisson Model
Example of a Simulated Normal Model
Generate a Random Transition Matrix
Get the Prior Probability of States
MCMC Sampler sampler for the Hidden Markov with Gamma-Poisson emission...
MCMC Sampler for the Hidden Markov Model with Normal emission densitie...
Simulate data distributed according to oHMMed with gamma-poisson emiss...
Simulate data distributed according to oHMMed with normal emission den...
Calculate a Continuous Approximation of the Kullback-Leibler Divergenc...
Calculate a Kullback-Leibler Divergence for a Discrete Distribution
oHMMed: HMMs with Ordered Hidden States and Emission Densities
Plot Diagnostics for hmm_mcmc_gamma_poisson Objects
Plot Diagnostics for hmm_mcmc_normal Objects
Forward-Backward Algorithm to Calculate the Posterior Probabilities of...
Forward-Backward Algorithm to Calculate the Posterior Probabilities of...
Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.
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