Results 161 to 170 of about 60,493 (305)
depmixS4: An R Package for Hidden Markov Models
depmixS4 implements a general framework for defining and estimating dependent mixture models in the R programming language. This includes standard Markov models, latent/hidden Markov models, and latent class and finite mixture distribution models.
Ingmar Visser, Maarten Speekenbrink
doaj
Background and Objectives: Tuberculosis is a chronic bacterial disease and a major cause of morbidity and mortality. It is caused by a Mycobacterium tuberculosis.
M Safari +3 more
doaj
Non-identifiability of the two state Markovian Arrival process [PDF]
In this paper we consider the problem of identifiability of the two-state Markovian Arrival process (MAP2). In particular, we show that the MAP2 is not identifiable and conditions are given under which two different sets of parameters, induce identical ...
Michael P. Wiper +2 more
core
Wind, waves, wing loading and the flight energetics of giant petrels
Read the free Plain Language Summary for this article on the Journal blog. Abstract Wind is a major factor driving seabird movement and energetics, the effects of which are modulated by morphology. Developments in tagging technology now make it possible to test predictions from aerodynamic theory about the effects of wind on flight performance in free ...
Madeline E. Hallet +3 more
wiley +1 more source
Hidden Markov models reveal ontogenetic plasticity in green and loggerhead sea turtles. [PDF]
Welsh RC, Mansfield KL.
europepmc +1 more source
Read the free Plain Language Summary for this article on the Journal blog. Abstract Balancing survival and reproduction presents a fundamental evolutionary challenge, especially in extreme and unpredictable environments. Thermoregulatory behaviour, in particular, imposes a costly trade‐off, as time spent maintaining optimal body temperature precludes ...
David L. Hubert +2 more
wiley +1 more source
Sparse nested Markov models with log-linear parameters
Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference.
Robins, James +7 more
core
Riding out the storm: Behavioural responses of a large herbivore to high‐Arctic winds
Using 11 years of GPS data from 61 muskoxen in Northeast Greenland, we show how increasing wind speed and Arctic storms reshape movement modes and habitat selection. Muskoxen respond by bedding in dense vegetation, prioritizing energy conservation over foraging, revealing a simple behavioural strategy with potential fitness consequences under ...
Floris M. van Beest +2 more
wiley +1 more source
Infinite hidden Markov models can dissect the complexities of learning. [PDF]
Bruijns SA +12 more
europepmc +1 more source
Multi-State Models for Panel Data: The msm Package for R
Panel data are observations of a continuous-time process at arbitrary times, for example, visits to a hospital to diagnose disease status. Multi-state models for such data are generally based on the Markov assumption.
Christopher Jackson
core

