Search results for "Markov"

A mathematical model by route of transmission and fibrosis progression to estimate undiagnosed individuals with HCV in different Italian regions.

2022

Abstract Background Although an increase in hepatitis C virus (HCV) prevalence from Northern to Southern Italy has been reported, the burden of asymptomatic individuals in different Italian regions is currently unknown. Methods A probabilistic approach, including a Markov chain for liver disease progression, was applied to estimate current HCV viraemic burden. The model defined prevalence by geographic area using an estimated annual historical HCV incidence by age, treatment rate, and migration rate from the Italian National database. Viraemic infection by age group was estimated for each region by main HCV transmission routes of individuals for stage F0–F3 (i.e. patients without liver cirr…

El análisis cuantitativo de trayectorias laborales. Un estado del arte

2022

La metodología cuantitativa aplicada al estudio de las trayectorias laborales ha experimentado un rápido auge que se ha extendido más allá del tradicional análisis de secuencias. El presente artículo es un estado del arte del desarrollo de nuevas técnicas estadísticas que pueden aplicarse o ya se aplican al estudio de trayectorias laborales. Además, incluimos sugerencias de software estadístico para la aplicación de cada una de las técnicas descritas. A lo largo de todo el texto, podrá observarse que la descripción de cada técnica se ha realizado desde un punto de vista conceptual, con el objetivo de llegar a un público amplio, que no necesite poseer una fuerte formación estadística. Es med…

Qualitative Analysis of Differential, Difference Equations, and Dynamic Equations on Time Scales

2015

and Applied Analysis 3 thank Guest Editors Josef Dibĺik, Alexander Domoshnitsky, Yuriy V. Rogovchenko, Felix Sadyrbaev, and Qi-Ru Wang for their unfailing support with editorial work that ensured timely preparation of this special edition. Tongxing Li Josef Dibĺik Alexander Domoshnitsky Yuriy V. Rogovchenko Felix Sadyrbaev Qi-Ru Wang

A Study of Perceptron Mapping Capability to Design Speech Event Detectors

2006

Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation f…

Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing

2020

In mission-critical, real-world environments, there is typically a low threshold for failure, which makes interaction with learning algorithms particularly challenging. Here, current state-of-the-art reinforcement learning algorithms struggle to learn optimal control policies safely. Loss of control follows, which could result in equipment breakages and even personal injuries.

Increasing sample efficiency in deep reinforcement learning using generative environment modelling

2020

CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

2020

Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error using explorative methods, such as \(\epsilon \)-greedy. There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines. Model-based RL learns a model of the environment for learning the policy while model-free approaches are fully explorative and exploitative without considering the underlying environment dynamics. Model-free RL works conceptually well in simulated environments, and empirical evidence suggests that trial and error lead to a near-opti…

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.