sequential data analysis using state space models with discrete latent variables and discrete or continuous observed variables

hidden Markov models can be used to detect discrete changes in the unobserved parameters that are generating a random process including the state at the present time and the probability of transitioning to a different state. the detection of regime switching is a common application thereof

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