Search results for "markov chain"
showing 10 items of 288 documents
A probabilistic expert system for predicting the risk of Legionella in evaporative installations
2011
Research highlights? The bacterium Legionella usually lives in water sources such as cooling towers. ? We discuss a probabilistic expert system for predicting the risk of Legionella. ? The expert system has a master-slave architecture. ? The inference engine is implemented through Bayesian reasoning. ? Bayesian networks model and connect relationships for chemical and physical variables. Early detection in water evaporative installations is one of the keys to fighting against the bacterium Legionella, the main cause of Legionnaire's disease. This paper discusses the general structure, elements and operation of a probabilistic expert system capable of predicting the risk of Legionella in rea…
Employing artificial neural networks to find reaction coordinates and pathways for self-assembly
2021
Capturing the autonomous self-assembly of molecular building blocks in computer simulations is a persistent challenge, requiring to model complex interactions and to access long time scales. Advanced sampling methods allow to bridge these time scales but typically require to construct accurate low-dimensional representations of the transition pathways. In this work, we demonstrate for the self-assembly of two single-stranded DNA fragments into a ring-like structure how autoencoder architectures based on unsupervised neural networks can be employed to reliably expose transition pathways and to provide a suitable low-dimensional representation. The assembly occurs as a two-step process throug…
Modeling NAFLD Disease Burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016-2030
2018
Background & Aims: Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are increasingly a cause of cirrhosis and hepatocellular carcinoma globally. This burden is expected to increase as epidemics of obesity, diabetes and metabolic syndrome continue to grow. The goal of this analysis was to use a Markov model to forecast NAFLD disease burden using currently available data. Methods: A model was used to estimate NAFLD and NASH disease progression in eight countries based on data for adult prevalence of obesity and type 2 diabetes mellitus (DM). Published estimates and expert consensus were used to build and validate the model projections. Results: If obesity and…
A methodology and algorithms for an optimal identification of Tourist Local Systems
2007
In last years, despite the emphasis on the importance of tourism as a leading industry in the development of a country’s economy, there is a lack of criteria and methodologies for the identification, the promotion and the governance of Tourism Local Systems (TLS). Moreover, nowadays an important debate is more and more emerging on the sustainable tourism development which involve three interconnected aspects: environmental, socio-cultural and economic. To this end, in this paper, a rigorous mathematical model is proposed for the optimal identification and dimensioning of TLS. The model here presented consists of a two stage methodology: at first, all the factors that characterize a geograph…
On the analysis of a new Markov chain which has applications in AI and machine learning
2011
Accepted version of an article from the conference: 2011 24th Canadian Conference on Electrical and Computer Engineering. Published version available from IEEE: http://dx.doi.org/10.1109/CCECE.2011.6030727 In this paper, we consider the analysis of a fascinating Random Walk (RW) that contains interleaving random steps and random "jumps". The characterizing aspect of such a chain is that every step is paired with its counterpart random jump. RWs of this sort have applications in testing of entities, where the entity is never allowed to make more than a pre-specified number of consecutive failures. This paper contains the analysis of the chain, some fascinating limiting properties, and some i…
Analysis of Low-Altitude Aerial Sequences for Road Traffic Diagnosis using Graph Partitioning and Markov Hierarchical Models
2016
International audience; This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from acquired sequences. Our approach begins with detecting the primitives of sequence images. At the time of this step of segmentation, the system computes dominant motion for each pair of images. This motion is computed using wavelets analysis on optical flow equation and robust techniques. Interesting areas (areas not affected by the dominant motion) are detected thanks to a Markov hierarchical model. Primitives stemming from segmentation and interesting a…
Probability and algorithmics: a focus on some recent developments
2017
Jean-François Coeurjolly, Adeline Leclercq-Samson Eds.; International audience; This article presents different recent theoretical results illustrating the interactions between probability and algorithmics. These contributions deal with various topics: cellular automata and calculability, variable length Markov chains and persistent random walks, perfect sampling via coupling from the past. All of them involve discrete dynamics on complex random structures.; Cet article présente différents résultats récents de nature théorique illustrant les interactions entre probabilités et algorithmique. Ces contributions traitent de sujets variés : automates cellulaires et calculabilité, chaînes de Mark…
Statistics of transitions for Markov chains with periodic forcing
2013
The influence of a time-periodic forcing on stochastic processes can essentially be emphasized in the large time behaviour of their paths. The statistics of transition in a simple Markov chain model permits to quantify this influence. In particular the first Floquet multiplier of the associated generating function can be explicitly computed and related to the equilibrium probability measure of an associated process in higher dimension. An application to the stochastic resonance is presented.
Variable Length Markov Chains, Persistent Random Walks: a close encounter
2020
This is the story of the encounter between two worlds: the world of random walks and the world of Variable Length Markov Chains (VLMC). The meeting point turns around the semi-Markov property of underlying processes.
Persistent random walks, variable length Markov chains and piecewise deterministic Markov processes *
2013
A classical random walk $(S_t, t\in\mathbb{N})$ is defined by $S_t:=\displaystyle\sum_{n=0}^t X_n$, where $(X_n)$ are i.i.d. When the increments $(X_n)_{n\in\mathbb{N}}$ are a one-order Markov chain, a short memory is introduced in the dynamics of $(S_t)$. This so-called "persistent" random walk is nolonger Markovian and, under suitable conditions, the rescaled process converges towards the integrated telegraph noise (ITN) as the time-scale and space-scale parameters tend to zero (see Herrmann and Vallois, 2010; Tapiero-Vallois, Tapiero-Vallois2}). The ITN process is effectively non-Markovian too. The aim is to consider persistent random walks $(S_t)$ whose increments are Markov chains with…