Search results for "Markov"
showing 10 items of 628 documents
Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation
2016
Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good seg…
Dinámica, modelización y servicios ecosistémicos del paisaje. Metodología para el análisis de la franja costera del Mediterráneo occidental
2019
El análisis y la dinámica de los paisajes a lo largo del tiempo y el espacio es esencial para la caracterización, ordenación y gestión de los paisajes actuales. Los paisajes, entendidos en su amplia escala espacial y temporal, son una expresión del trabajo conjunto de la naturaleza y el ser humano y por tanto un recurso territorial, un patrimonio y una señal de identidad, que necesita atención, protección y gestión. En el Convenio Europeo del Paisaje se institucionaliza el interés y el derecho al paisaje, y se insta a que los estados y regiones firmantes articulen políticas de paisaje en el marco de la ordenación territorial. Políticas que se operativizan mediante la identificación, análisi…
Analysing Complex Life Sequence Data with Hidden Markov Modelling
2016
When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling. In the first approach we used SA clusters as fixed and estimated HMMs separately for each group. In the second approach we treated SA clusters as suggestive and …
HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment
2016
The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology; this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a…
Predictive control of convex polyhedron LPV systems with Markov jumping parameters
2012
The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon…
Modeling long-range memory with stationary Markovian processes
2009
In this paper we give explicit examples of power-law correlated stationary Markovian processes y(t) where the stationary pdf shows tails which are gaussian or exponential. These processes are obtained by simply performing a coordinate transformation of a specific power-law correlated additive process x(t), already known in the literature, whose pdf shows power-law tails 1/x^a. We give analytical and numerical evidence that although the new processes (i) are Markovian and (ii) have gaussian or exponential tails their autocorrelation function still shows a power-law decay =1/T^b where b grows with a with a law which is compatible with b=a/2-c, where c is a numerical constant. When a<2(1+c) th…
KERNEL ESTIMATION OF THE TRANSITION DENSITY IN BIFURCATING MARKOV CHAINS
2023
We study the kernel estimator of the transition density of bifurcating Markov chains. Under some ergodic and regularity properties, we prove that this estimator is consistent and asymptotically normal. Next, in the numerical studies, we propose two data-driven methods to choose the bandwidth parameters. These methods are based on the so-called two bandwidths approach.
Robust delay-dependent H∞ control of uncertain time-delay systems with mixed neutral, discrete, and distributed time-delays and Markovian switching p…
2011
Author's version of an article published in the journal: IEEE Transactions on Circuits and Systems I: Regular Papers. Also available from the publisher at: http://dx.doi.org/10.1109/tcsi.2011.2106090 The problem of robust mode-dependent delayed state feedback H ∞ control is investigated for a class of uncertain time-delay systems with Markovian switching parameters and mixed discrete, neutral, and distributed delays. Based on the LyapunovKrasovskii functional theory, new required sufficient conditions are established in terms of delay-dependent linear matrix inequalities for the stochastic stability and stabilization of the considered system using some free matrices. The desired control is …
Statistical properties of general Markov dynamical sources: applications to information theory
2004
In \textitDynamical sources in information theory: fundamental intervals and word prefixes, B. Vallée studies statistical properties of words generated by dynamical sources. This is done using generalized Ruelle operators. The aim of this article is to generalize sources for which the results hold. First, we avoid the use of Grotendieck theory and Fredholm determinants, this allows dynamical sources that cannot be extended to a complex disk or that are not analytic. Second, we consider Markov sources: the language generated by the source over an alphabet \textbfM is not necessarily \textbfM^*.
Population processes under the influence of disasters occurring independently of population size
1989
Markov branching processes and in particular birth-and-death processes are considered under the influence of disasters that arrive independently of the present population size. For these processes we derive an integral equation involving a shifted and rescaled argument. The main emphasis, however, is on the (random) probability of extinction. Its distribution density satisfies an equation which can be solved numerically at least up to a multiplicative constant. In an example it is also found by simulation.