Browsing Bergen Open Research Archive by Author "Bacri, Timothee Raphael Ferdinand"
Now showing items 1-4 of 4
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Computational issues in parameter estimation for hidden Markov models with template model builder
Bacri, Timothee Raphael Ferdinand; Berentsen, Geir Drage; Bulla, Jan; Støve, Bård (Journal article; Peer reviewed, 2023)A popular way to estimate the parameters of a hidden Markov model (HMM) is direct numerical maximization (DNM) of the (log-)likelihood function. The advantages of employing the TMB [Kristensen K, Nielsen A, Berg C, et al. ... -
Effects of complex neck therapy – kinesiotherapy and interspinal muscles massage – on tinnitus
Spencer, Shikha; Sereda, Magdalena; Marzena, Bielińska; Olszewski, Jurek; Adebusoye, Busola; Sobkiewicz, Adam; Bacri, Timothee Raphael Ferdinand; Bulla, Jan; Mielczarek, Marzena (Journal article, 2023)Introduction: Past studies have shown connections between the somatosensory system (the neck and temporomandibular joint region) and the auditory system. It is therefore likely that tinnitus patients might benefit from ... -
A gentle tutorial on accelerated parameter and confidence interval estimation for hidden Markov models using Template Model Builder
Bacri, Timothee Raphael Ferdinand; Berentsen, Geir Drage; Bulla, Jan; Hølleland, Sondre Nedreås (Journal article; Peer reviewed, 2022)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 ... -
Testing for time-varying nonlinear dependence structures: Regime-switching and local Gaussian correlation
Gundersen, Kristian; Bacri, Timothee Raphael Ferdinand; Bulla, Jan; Hølleland, Sondre Nedreås; Maruotti, Antonello; Støve, Bård (Journal article; Peer reviewed, 2024)This paper examines nonlinear and time-varying dependence structures between a pair of stochastic variables, using a novel approach which combines regime-switching models and local Gaussian correlation (LGC). We propose ...