Results 1 to 10 of about 139,337 (213)
Flexible hidden Markov models for behaviour-dependent habitat selection
bioRxiv, 2022 Background There is strong incentive to model behaviour-dependent habitat selection, as this can help delineate critical habitats for important life processes and reduce bias in model parameters.
NJ Klappstein, L Thomas, T. Michelot
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ANALYSIS OF WEATHER CHANGES FOR ESTIMATION OF SHALLOT CROPS FLUCTUATION USING HIDDEN MARKOV
BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN, 2022 Climate change has an impact on increasing the temperature of the earth's surface or what is known as global warming. The impact of global warming will affect the pattern of precipitation, evaporation, water run-off, soil moisture and climate variations ...
Yan Aditya Pradana +3 more
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Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process [PDF]
2019 American Control Conference (ACC), 2019 The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets. When the full state observation is available, Q-learning finds the optimal action-value function given the current action (Q function).
Yoon, Hyung-Jin +2 more
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Frontiers in Behavioral Economics, 2023 Hidden Markov Models (HMMs) are used to study language, sleep, macroeconomic states, and other processes that reflect probabilistic transitions between states that can't be observed directly. This paper applies HMMs to data from location-based game theory experiments. In these location games, players choose a pixel location from an image. These players
Xiaomin Li +2 more
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Maximum likelihood estimation in hidden Markov models with inhomogeneous noise [PDF]
E S A I M: Probability & Statistics, 2018 We consider parameter estimation in finite hidden state space Markov models with time-dependent inhomogeneous noise, where the inhomogeneity vanishes sufficiently fast.
M. Diehn, A. Munk, Daniel Rudolf
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Time-delay estimation for compound point-processes using hidden Markov models [PDF]
1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings, 2002 A new time-delay estimation algorithm for compound point-processes is presented. Compound point-processes, a generalization of temporal point-processes, describe processes with discrete events, where each occurrence time is associated with certain features.
Wohlers, Jens +2 more
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Characterising Eye Movement Events with an Unsupervised Hidden Markov Model
Journal of Eye Movement Research, 2022 Eye-tracking allows researchers to infer cognitive processes from eye movements that are classified into distinct events. Parsing the events is typically done by algorithms.
Malte Lüken, Š. Kucharský, I. Visser
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Nonparametric inference in hidden Markov models using P‐splines [PDF]
Biometrics, 2013 Hidden Markov models (HMMs) are flexible time series models in which the distribution of the observations depends on unobserved serially correlated states.
Roland Langrock +3 more
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Handling underlying discrete variables with bivariate mixed hidden Markov models in NONMEM
Journal of Pharmacokinetics and Pharmacodynamics, 2019 Non-linear mixed effects models typically deal with stochasticity in observed processes but models accounting for only observed processes may not be the most appropriate for all data.
A. Brekkan +3 more
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Hidden Markov Models in Time Series, with Applications in Economics
Handbook of Mixture Analysis, 2016 Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process.
S. Kaufmann
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