Global ETD Search
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Showing 1 to 20 of 245 for “"Markov Models"”.
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Experiments with hidden Markov models
… on the results of our experiments with hidden Markov models, focusing on Markov chains with generative transitions and the Baum-Welch algorithm. We explore generating the hypothesis model in various ways. We use the hypothesis model as the original model. And we investigate the feasibility of …
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Bandits in autoregressive Markov models
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01
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Card counting meets hidden Markov models
The Hidden Markov Model (HMM) is a stochastic process that involves an unobservable Markov Chain and an observable output at each state in the chain. Hidden Markov Models are described by three parameters: A, B, and \uf070. A is a matrix that holds the transition probabilities for the unobservable …
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Spectral Estimation of Hidden Markov Models
… methods for estimating key quantities of hidden Markov models through spectral method-of-moments estimation. Unlike traditional estimation methods like EM and Gibbs sampling, the set of estimation methods, which we call spectral HMMs (sHMMs), are incredibly fast, do not require multiple restarts, …
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Face recognition using Hidden Markov Models
… using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 …
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Option pricing using hidden Markov models
… expression for option prices: the Hidden Markov Option Pricing Model. This is possible due to the macro-structure of this model and provides the added advantage of ensuring efficient computation of option prices. This model turns out to be a very natural extension to the Black-Scholes …
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Model reduction for Hidden Markov models
… of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. In the Jump Linear Systems …
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Hidden Markov models for gesture recognition
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Cellular diagnostic systems using hidden Markov models
… In this research we propose that Hidden Markov Models (HMMs) be used as a method to infer signature statistics about the nature and sources of faults in a cellular system by fitting models to various time-series data measured throughout the network. By including HMMs in the network …
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Administración algorítmica de portafolio con Hidden Markov models
… Se hace énfasis en el uso de los Hidden Markov Models para mejorar la estrategia de trading y la administración del portafolio.
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Bounded Aggregation Techniques to Solve Large Markov Models
"Markovian modeling of systems is a promising technique used to gauge the performance, dependability, and performability of systems. It can be used to aid design decisions by evaluating a range of design options for a range of environments, and it can be used to increase understanding of …
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Autoregressive hidden Markov models and the speech signal
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. This new variant of the HMM is built upon the mathematical structure of the HMM and linear prediction analysis of speech signals. By incorporating these two methods into one …
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Hierarchical modeling for reliability analysis using Markov models
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Two new approaches for learning Hidden Markov Models
Hidden Markov Models (HMMs) are ubiquitously used in applications such as speech recognition and gene prediction that involve inferring latent variables given observations. For the past few decades, the predominant technique used to infer these hidden variables has been the Baum-Welch algorithm. …
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On-line handwriting recognition using hidden Markov models
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Inventory estimation from transactions via hidden Markov models
… tracking in the retail industry using Hidden Markov Models. It has been observed that inventory records are extremely inaccurate in practice (cf. [1{4]). Reasons for this inaccuracy are item losses due to item theft, mishandling, etc. which are unaccounted. Even more important are the lost …
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Statistical Analysis of Wireless Systems Using Markov Models
… of this dissertation deals with discrete channel models that are used for simulating error traces produced by wireless channels. Most of the time, wireless channels have memory and we rely on discrete time Markov models to simulate them. The primary advantage of using these models is rapid …
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Automatic Phoneme Recognition with Segmental Hidden Markov Models
… of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for the …
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