A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder.

ABSTRACT: A very common way to estimate the parameters of a hidden Markov model (HMM) is the relatively straightforward computation of maximum likelihood (ML) estimates. For this task, most users rely on user-friendly implementation of the estimation routines via an interpreted programming language such as the statistical software environment R. Such an approach can easily require time-consuming computations, in particular for longer sequences of observations. In addition, selecting a suitable approach for deriving confidence intervals for the estimated parameters is not entirely obvious, and often the computationally intensive bootstrap methods have to be applied. In this tutorial, we illustrate how to speed up the computation of ML estimates significantly via the R package TMB. Moreover, this appr

SUBMITTER: Bacri T 

PROVIDER: S-EPMC9796807 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature