Results 31 to 40 of about 9,580 (220)
Bayesian nonparametric hidden Markov models with application to the analysis of copy-number-variation in mammalian genomes [PDF]
, 2009 We consider the development of Bayesian Nonparametric methods for product partition models such as Hidden Markov Models and change point models.
Yau, C. +3 more
core
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
HMM based scenario generation for an investment optimisation problem [PDF]
, 2011 This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series.
Erlwein, C +5 more
core +1 more source
PPO‐Based Reinforcement Learning for the Semi‐Active Vibration Control of MDOF Platform
AI &Innovation, EarlyView.ABSTRACT Aiming at the coupled vibration problem of a multi‐degree‐of‐freedom (MDOF) vibration isolation platform under eccentric excitation, this paper proposes a semi‐active vibration control strategy based on Proximal Policy Optimization (PPO) ‐based reinforcement learning (PPO RL).
Wei Huang, Jian Xu
wiley +1 more source
Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. [PDF]
, 2008 We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as
Fearnhead, Paul, Paul Fearnhead
core +1 more source
Estimating Components in Finite Mixtures and Hidden Markov Models [PDF]
When the unobservable Markov chain in a hidden Markov model is stationary the marginal distribution of the observations is a finite mixture with the number of terms equal to the number of the states of the Markov chain.
D.S. Poskitt, Jing Zhang
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Biological Reviews, EarlyView.ABSTRACT Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip‐dating approaches, including fossil data, for inference of time‐scaled trees ...
Melanie J. Hopkins +9 more
wiley +1 more source
Business Strategy and the Environment, EarlyView.ABSTRACT The concept of predictive maintenance in advanced manufacturing systems is crucial from the point of view of resource efficiency in the era of high competitiveness forced by energy transformation in the digital economy. Against the backdrop of sustainability and the opportunities a data cooperative offers, the combination of predictive ...
Christian Schachtner +6 more
wiley +1 more source
Option pricing using hidden Markov models
, 2006 Includes bibliographical references (leaves 144-149).This work will present an option pricing model that accommodates parameters that vary over time, whilst still retaining a closed-form expression for option prices: the Hidden Markov Option Pricing ...
Anderson, Michael
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An analysis of the exponential decay principle in probabilistic trust models
, 2009 Research in models for experience-based trust management has either ignored the problem of modelling and reasoning about dynamically changing principal behaviour, or provided ad hoc solutions to it. Probability theory provides a foundation for addressing
Krukow, Karl Tikjøb +5 more
core +1 more source