Results 141 to 150 of about 73,803 (311)
The Realization Problem for Hidden Markov Models
Let \(X\) be a discrete-time finite-state Markov process and let \(Y=h(X,Z)\), where \(Z\) is some i.i.d. sequence, and where the output \(Y\) can take a finite number of values. This paper considers the realization question: given the probabilities of all finite-length output strings, under what circumstances and how can one construct an \(X\) as ...
openaire +1 more source
Reduced complexity on-line estimation of hidden Markov model parameters
In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates,
Moore, John B., Ford, Jason J.
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Epistemic and aleatoric uncertainty quantification in weather and climate models
Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield +1 more
wiley +1 more source
Bayesian inference for Hidden Markov Model
Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of ...
Luisa Scaccia, Rosella Castellano
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ABSTRACT This paper addresses the problem of dynamic output‐feedback H∞$$ {H}_{\infty } $$ detector‐based control for continuous‐time Markov Jump Lur'e Systems with uncertain transition rate matrices. In contrast to conventional approaches, the proposed synthesis conditions are derived using Finsler's lemma, introducing additional slack variables to ...
Lucas P. M. Silva +2 more
wiley +1 more source
Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series
Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs) provide flexible, general-purpose models for univariate and multivariate time series.
Bulla, Jan
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ABSTRACT Pacific salmon face substantial challenges when migrating through anthropogenically modified river systems, such as the Sacramento‐San Joaquin River Delta (the Delta). Non‐physical behavioral barriers, such as the bioacoustic fish fence (BAFF), are one potential solution for guiding fish away from hazards without obstructing water flow ...
Maggie Raboin +2 more
wiley +1 more source
Network Latency Estimation for Telesurgery Using Deep Reinforcement Learning
Overview of the proposed two‐stage deep reinforcement learning framework for network latency prediction in telesurgery. The pipeline includes data collection from simulated catheter navigation sessions (Philippines–Botswana), feature engineering, DQN‐based direction prediction (85.8% accuracy), direction‐to‐value transformation, and value forecasting ...
Bakang Kgopolo +2 more
wiley +1 more source
Accommodating availability bias on line transect surveys using hidden Markov models.
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000458/Maximum likelihood methods are developed which accommodate intermittent animal availability of animals on line transect surveys.
Samara, Filipa I. P., Borchers, David L.
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Abstract Our generation inherits this cultural heritage of historic material and historic reinforced concrete structures and thus bears a certain responsibility to preserve these historic buildings with the help of the new technologies of lifetime management, conservation concepts and the new digitalization as well as the emerging safety concepts of ...
A. Strauss
wiley +1 more source

