Search results for "markov chain"
showing 10 items of 288 documents
Global exponential stability of delayed Markovian jump fuzzy cellular neural networks with generally incomplete transition probability
2014
The problem of global exponential stability in mean square of delayed Markovian jump fuzzy cellular neural networks (DMJFCNNs) with generally uncertain transition rates (GUTRs) is investigated in this paper. In this GUTR neural network model, each transition rate can be completely unknown or only its estimate value is known. This new uncertain model is more general than the existing ones. By constructing suitable Lyapunov functionals, several sufficient conditions on the exponential stability in mean square of its equilibrium solution are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is presented to illustrate the effectiveness and efficiency of our res…
Web Usage Mining by Neural Hybrid Prediction with Markov Chain Components
2021
This paper presents and evaluates a two-level web usage prediction technique, consisting of a neural network in the first level and contextual component predictors in the second level. We used Markov chains of different orders as contextual predictors to anticipate the next web access based on specific web access history. The role of the neural network is to decide, based on previous behaviour, whose predictor’s output to use. The predicted web resources are then prefetched into the cache of the browser. In this way, we considerably increase the hit rate of the web browser, which shortens the load times. We have determined the optimal configuration of the proposed hybrid predictor on a real…
Dark coupling and gauge invariance
2010
We study a coupled dark energy–dark matter model in which the energymomentum exchange is proportional to the Hubble expansion rate. The inclusion of its perturbation is required by gauge invariance. We derive the linear perturbation equations for the gauge invariant energy density contrast and velocity of the coupled fluids, and we determine the initial conditions. The latter turn out to be adiabatic for dark energy, when assuming adiabatic initial conditions for all the standard fluids. We perform a full Monte Carlo Markov Chain likelihood analysis of the model, using WMAP 7-year data.
Recent Advances in Bayesian Inference in Cosmology and Astroparticle Physics Thanks to the MultiNest Algorithm
2012
We present a new algorithm, called MultiNest, which is a highly efficient alternative to traditional Markov Chain Monte Carlo (MCMC) sampling of posterior distributions. MultiNest is more efficient than MCMC, can deal with highly multi-modal likelihoods and returns the Bayesian evidence (or model likelihood, the prime quantity for Bayesian model comparison) together with posterior samples. It can thus be used as an all-around Bayesian inference engine. When appropriately tuned, it also provides an exploration of the profile likelihood that is competitive with what can be obtained with dedicated algorithms.
Retrieval of atmospheric CH4profiles from Fourier transform infrared data using dimension reduction and MCMC
2016
We introduce an inversion method that uses dimension reduction for the retrieval of atmospheric methane (CH4) profiles. Uncertainty analysis is performed using the Markov chain Monte Carlo (MCMC) statistical estimation. These techniques are used to retrieve CH4 profiles from the ground-based spectral measurements by the Fourier Transform Spectrometer (FTS) instrument at Sodankyla (67.4 degrees N, 26.6 degrees E), Northern Finland. The Sodankyla FTS is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the data provides approximately 3 degrees of freedom about the vertical struc…
Bayesian dynamic modeling of time series of dengue disease case counts
2017
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order …
The Extinction of Generations in Generation-Dependent Bellman-Harris Branching Processes with Exponential Lifespan
1978
If V is the time when in a Bellman-Harris branching model the k-th generation disappears out of the population, and if all individuals have exponentially distributed lifespans, the asymptotic behavior of the tail of the distribution of the extinction time V , P(V > t), is obtained, even if the distributions of the lifespans and the offspring sizes vary generation-dependent. Furthermore the times of extinction of several successive generations can be specified for the generation- independent case of the Markov branching model in continuous time. If the initial number of individuals and the absolute time grow up appropriately linked, a Poisson limit theorem for generation sizes will be given.
Overall asthma control: the relationship between current control and future risk.
2009
Background Asthma guidelines emphasize both maintaining current control and reducing future risk, but the relationship between these 2 targets is not well understood. Objective This retrospective analysis of 5 budesonide/formoterol maintenance and reliever therapy (Symbicort SMART Turbuhaler ∗ ∗Symbicort SMART and Turbuhaler are trademarks owned by AstraZeneca. Neither the Symbicort SMART posology nor the dry powder formulation Turbuhaler are currently approved in the United States.) studies assessed the relationship between asthma control questionnaire (ACQ-5) and Global Initiative for Asthma-defined clinical asthma control and future risk of instability and exacerbations. Methods The perc…
Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation
2014
[EN] With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocate…
Constraint Cellular Automata for Urban Development Simulation: An Application to the Strasbourg-Kehl Cross-Border Area
2017
AcknowledgementsThe research presented in this chapter is part of the Smart. Boundary project supported by the Fonds National de la Recherche in Luxembourg and CNRS in France (ref. INTER/CNRS/12/02). The authors would like also to thank the Grasp Program of LISER for allowing cross-collaboration between the two teams based in Luxembourg and France.; International audience; Urban sprawl and space consumption have become key issues in sustainable territorial development. Traditional planning approaches are often insufficient to anticipate their complex spatial consequences, especially in cross-border areas. Such complexity requires the use of dynamic spatial simulations and the development of…