state-space Markov models
Identification of speculative bubbles using state-space models with Markov-switching
... econometric models, which have been used intensively for the de- tection of bubble components, are so-called Markov-switching (or regime-switching) ...These models are designed to capture discrete ...
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Markov chain Monte Carlo methods for state space models with point process observations
... In introducing this model, Smith and Brown (2003) derived an approx- imate expectation-maximization (EM) algorithm for parameter estimation and state inference. In subsequent work, it was shown that the ...
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Nonlinear Markov games on a finite state space (mean field and binary interactions)
... The closest to our setting seems to be the recent work (Gast, Gaujal, & Le Boudec, 2010), which is devoted to a con- vergence result similar to our Theorem 2. However, in (Gast, Gaujal, & Le Boudec, 2010) a ...
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Stochastic Gradient MCMC for Nonlinear State Space Models
... Nonlinear state space models (SSMs) are widely used in many scientific domains for modeling time series and sequential ...disease models, Dukic et ...volatility models, Shephard ...
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Cyclic Markov chains with an application to an intermediate ENSO model
... tial state errors within the linear framework of singular vec- tors (Lorenz, ...atmosphere-ocean models (Chen et ...linearized models and their adjoint can be obtained without facing fundamental ...
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Towards Relational POMDPs for Adaptive Dialogue Management
... Observable Markov De- cision Process (POMDP) over a rich state space incorporating both dialogue, user, and environment ...action space based on prior knowledge over locally relevant dialogue ...
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Reliability analysis techniques explored through a communication network example
... Figure 8: Instantaneous Availability of the network practical situations, this assumption does not hold. A single repair facility shared among all components, dif- ferent priorities assigned to repair of dierent compo- ...
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Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation
... discrete state value is too re- strictive, as it has been shown in the literature that words have many different features in a multi- dimensional space where they could be separately characterized as ...
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Particle Filters and Data Assimilation
... State-space models can be used to incorporate subject knowledge on the underlying dynamics of a time series by the introduction of a latent Markov ...the state relates to partial and ...
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State Space Models in Medical Time Series
... the state transition retains the features of the linear Markov model of order ...the state posterior distribution of the DGLM is not analyt- ically ...the state posterior ...
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Markov Switching Models with state dependent time varying transition probabilities
... regime-switching models with time-inhomogeneous Markov transitions is which variables help to predict the tran- sitions between different regimes (a period of relative calm and a financial crisis, ...
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Non homogeneous Markov chains with a finite state space and a Doeblin type theorem
... The second step is to consider restrictions of the sequence of blocks each block is a product of length da to an essential class with period da; these restrictions are allowable nonnegat[r] ...
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State Space Modelling Using Particle Filtering
... engineering state modelling takes leading role. In state modelling, On-line state estimation is a key ...When state functions are highly non-linear and the posterior probability of the ...
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Ontological evaluation of business models: comparing traditional and component-based paradigms in information systems re-engineering
... ER models, OML and UML (Evermann and Wand 2001; Green and Rosemann 2000; Opdahl and Henderson-Sellers 2002; Weber and Zhang 1996) and for the evaluation of enterprise systems (Green et ...
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Generalizations of Markov Chain Discretizations
... Our desire to measure how close our discrete-time approximations are to the continuous-time matrix ex- ponential necessitates the introduction of a metric on the space of transition matrices and on the ...
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Application of Hidden Markov Models and Hidden Semi Markov Models to Financial Time Series
... The decision to work on the first topic was motivated by the fact that the parameters of a HMM can be estimated by direct numerical maximization (DNM) of the log-likelihood function or, more popularly, using the ...
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Linear State-Space Models for Blind Source Separation
... general models and estimation schemes; most current work is highly application specific with the majority focused on applications in separation of speech ...
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Model-Based Diagnosis of Hybrid Systems Using Satisfiability Modulo Theory
... Given the vector C 0 as COMPS and the system description SD it is possible to employ any diagnosis algorithm such as CDA* (Williams and Ragno 2007), GDE (Reiter 1987), or SATbD (Metodi et al. 2014) to find minimum ...
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Deep Abstractions of Chemical Reaction Networks
... Our implementation is in Python, and builds on several available tools and libraries, developed in the research communities of deep learning and computa- tional systems biology. Integrating tools of di↵erent communities ...
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Book Reviews: Computational Approaches to Morphology and Syntax by Brian Roark and Richard Sproat
... Reduplication effects, of course, challenge finite-state explanations, and so receive their own section. Extended (non-regular) computational models are presented along- side data from Gothic, Dakota, and ...
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