[PDF] Top 20 Gaussian Conditionally Markov Sequences: Theory with Application
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Gaussian Conditionally Markov Sequences: Theory with Application
... Modeling and predicting trajectories with an intent or a destination have been studied in the literature. This problem has two steps: (a) trajectory modeling, (b) trajectory processing (filtering and prediction). The ... See full document
127
Application of queueing theory in bank sectors
... Queuing theory is the study of waiting in all these various ...Queuing Theory and its empirical analysis based on the observed data of service in ...the application of a mathematical model is to ... See full document
7
Mapping Activity Diagram to Petri Net: Application of Markov Theory for Analyzing Non-Functional Parameters
... Abstract The quality of an architectural design of a software system has a great influence on achieving non-functional requirements of a system. A regular software development project is often influenced by ... See full document
12
Topics in the theory and applications of Markov chains
... Still continuing with the case m < 1 and Q R-positive, let us consider an application of Theorem 1.3 when the chain {Z^} starts from a specified ’ancestor* distribution {n\}. It is convenient to use criterion ( ... See full document
145
Markov renewal theory applied to performability evaluation
... The inductive approach does not have any general formulation. Its application varies from case to case, though the contructive process of the matrices follows approximately a regular pattern. We illustrate this ... See full document
44
Aggregation of Markov flows I : theory
... the Markov case, this can not be done with the above schemes because the dynamics of an N -state Markov chain has N − 1 eigenvalues (counting multiplicity) so reduction of N must lose some of the ... See full document
16
Particle Gibbs with Ancestor Sampling
... In this paper we present a new tool in the family of Monte Carlo methods which is par- ticularly useful for inference in SSMs and, importantly, in non-Markovian latent variable models. However, the proposed method is by ... See full document
40
Discretizing Nonlinear, Non Gaussian Markov Processes with Exact Conditional Moments
... To illustrate the general applicability of our method, in this section we solve an asset pricing model with variable rare disasters (Gabaix, 2012). There are several good reasons to consider this model. First, the ... See full document
55
Gaussian Markov Random Field Models for Surveillance Error and Geographic Boundaries
... The interest of this section is modeling spatial processes on non-flat surfaces and the ac- companying statistical inference. While all the surfaces in consideration are subsets of Euclidean space, the application ... See full document
85
Switching Markov Gaussian models for dynamic power system inertia estimation
... Abstract— Future power systems could benefit considerably from having a continuous real-time estimate of system inertia. If realized, this could provide reference inputs to proactive control and protection systems which ... See full document
10
Small ball probabilities for Gaussian Markov processes under the L-norm
... Other related facts and history are given after Lemma 2.3. The sup-norm case (p = ∞) of the above result was presented in Li (1999a). Second we observe that there is nothing special about the interval [0; 1] and it can ... See full document
16
Faster quantum mixing for slowly evolving sequences of Markov chains
... of Markov chains which, for in- stance, encode the Gibbs (thermal) distributions at gradually decreasing values of the temperature, where the target distribution is specified by the final MC, ... See full document
20
Gaussian Distributive Stochastic Neighbor Embedding Based Feature Extraction for Medical Data Diagnosis
... of Gaussian Distributive Stochastic Neighbor Embedding (GDSNE) in proposed GDSNE-FE technique where it applied Gaussian probability distribution and Jensen–Shannon Divergence to extract only optimal ... See full document
14
Some problems in the theory and applications of Markov chains
... XV doubly stochastic matrix, and another derived from it, The chapter ends with a review of the case of the Markov chain with three states, for which these results prove to be exhaustive[r] ... See full document
207
Multiresolution Gaussian mixture models : theory and applications
... The left image, , shows the re onstru tion using only spatial information, using a `soft' de ision: ea h pixel is treated as a mixture, with weights given by the relative magnitudes of [r] ... See full document
28
Theory of genuine tripartite nonlocality of Gaussian states
... a Gaussian state, we can reformulate them all in terms of CMs of the state and its marginals, adopting once more displaced parity measurement operators P j ... See full document
6
Markov denumerable process and queue theory
... the Markov chain for they have many similar ...a Markov process follows either one of two differential equations, which are called the Kolmogorov backward and forward ...of Markov chains. Recent two ... See full document
128
Modeling of Video Sequences by Gaussian Mixture: Application in Motion Estimation by Block Matching Method
... This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, ... See full document
8
Applying Hidden Markov Model to Protein Sequence Alignment
... hidden Markov models (HMMs) have several advantages over standard ...consistent theory behind gap and insertion scores, in contrast to standard profile methods which use heuristic ...aligned ... See full document
5
Nonlinear Markov semigroups and interacting Lévy type processes
... way that more or less straightforwardly extends to the Markov models of interactions not preserving the number of particles thus including the processes of, say, coagulation and fragmentation. In Section 7 we ... See full document
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