Results 121 to 130 of about 29,628 (317)
Probabilistic Programming Process Algebra
, 2014 Formal modelling languages such as process algebras are widespread and effective tools in computational modelling. However, handling data and uncertainty in a statistically meaningful way is an open problem in formal modelling, severely hampering the ...
Sanguinetti, Guido +7 more
core +1 more source
Insight of Elementary Steps on the Polyethylene and Polypropylene Co‐Pyrolysis
Carbon and Hydrogen, EarlyView.Pyrolysis of PP initiates first, accelerating PE cracking via secondary radical transfer, leading to n‐hydrocarbon yield enhancement. Py–GC/MS and nanoreactor computation are in agreement on the elementary step proposal. ABSTRACT Pyrolysis with gas chromatography & mass spectrometry (Py–GC/MS) technique was employed to analyze the yield variation of ...
Naiwen Xu +6 more
wiley +1 more source
A hidden Markov model and reinforcement learning‐based strategy for fault‐tolerant control
The Canadian Journal of Chemical Engineering, EarlyView.Abstract This study introduces a data‐driven control strategy integrating hidden Markov models (HMM) and reinforcement learning (RL) to achieve resilient, fault‐tolerant operation against persistent disturbances in nonlinear chemical processes. Called hidden Markov model and reinforcement learning (HMMRL), this strategy is evaluated in two case studies
Tamera Leitao +2 more
wiley +1 more source
Coalescence time and second largest eigenvalue modulus in the monotone reversible case [PDF]
If T is the coalescence time of the Propp and Wilson [15], perfect simulation algorithm, the aim of this paper is to show that T depends on the second largest eigenvalue modulus of the transition matrix of the underlying Markov chain.
Leisen Fabrizio, Mira Antonietta
core
Geodesic convexity of the relative entropy in reversible Markov chains [PDF]
, 2011 We consider finite-dimensional, time-continuous Markov chains satisfying the detailed balance condition as gradient systems with the relative entropy E as driving functional.
Mielke, Alexander
core +1 more source
A Hierarchical Approach to Generating Maps Using Markov Chains
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2014 In this paper we describe a hierarchical method for procedurallygenerating maps using Markov chains. Ourmethod takes as input a collection of human-authoredtwo-dimensional maps, and splits them into high-leveltiles which capture large structures. Markov chains arethen learned from those maps to capture the structure ofboth the high-level tiles, as well
Sam Snodgrass, Santiago Ontañón
openaire +2 more sources
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
Acta Universitatis Sapientiae: Economics and Business, 2015 The telecommunication industry is one of the service industries that is most affected by the problem of subscribers’ churn. Although several techniques have been used to predict customer churn in developed countries, many of those studies used secondary ...
Adebiyi Sulaimon Olanrewaju +2 more
doaj +1 more source
Improving Sampling from Generative Autoencoders with Markov Chains
CoRR, 2016 We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model.
Arulkumaran, K, Creswell, A, Bharath, AA
openaire +3 more sources
Hidden Markov graphical models with state‐dependent generalized hyperbolic distributions
Canadian Journal of Statistics, EarlyView.Abstract In this article, we develop a novel hidden Markov graphical model to investigate time‐varying interconnectedness between different financial markets. To identify conditional correlation structures under varying market conditions and accommodate shape features embedded in financial time series, we rely upon the generalized hyperbolic family of ...
Beatrice Foroni +2 more
wiley +1 more source