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IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 12/2016, Volume 38, Issue 12, pp. 2487 - 2500
We propose minimum entropy rate simplification (MERS), an information-theoretic, parameterization-independent framework for simplifying generative models of... 
synthesis | signal analysis | language generation | Stochastic processes | and processing | Distortion | Electronic mail | Gaussian processes | statistical models | Markov processes | Signal processing | Density functional theory | information theory | H.1.1.b Information theory | H.5.5.c Signal analysis, synthesis, and processing | I.2.7.b Language generation | I.5.1.e Statistical models | G.3.p Stochastic processes | G.3.e Markov processes | stochastic processes | CAPACITY | INFORMATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | DIVERGENCE | SELECTION | Algorithms | Animals | Models, Biological | Computer Simulation | Humans | Entropy | Models, Statistical | Pattern Recognition, Automated | Sound Spectrography - methods | Speech Production Measurement - methods | Machine Learning | Usage | Research | Electric distortion | Divergence | Simplification | Noise reduction | Smoothing | Mathematical models | Signal theory | Teknik och teknologier | Random processes | Elektroteknik och elektronik | Image coding | Analytic solution | Gaussian Processes | Kullback Leibler divergence | Divergence constraint | Electrical Engineering, Electronic Engineering, Information Engineering | Classification models | Engineering and Technology | H.5.5.c Signal analysis | Stochastic systems | Rate distortion-theory | Language generation | Stochastic models | Dissimilarity measures | Signal distortion | Information theory
Journal Article
Applied stochastic models and data analysis, ISSN 8755-0024, 1985
Journal
Neural Computation, ISSN 0899-7667, 12/2016, Volume 28, Issue 12, p. 2853
  This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we... 
Learning | Stochastic models | Markov analysis | Risk management | Artificial intelligence
Journal Article
Reviews of Modern Physics, ISSN 0034-6861, 01/2013, Volume 85, Issue 1, pp. 135 - 196
Journal Article
Journal of the Operational Research Society, ISSN 0160-5682, 11/2015, Volume 66, Issue 11, pp. 1840 - 1849
Stochastic processes are natural models for the progression of many individual and team sports. Such models have been applied successfully to select strategies... 
Markov processes | time series | learning | sports | stochastic processes | decision analysis | TARGET | PROCESS MODEL | MANAGEMENT | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | SOCCER | COMPETITION | SCORES | FOOTBALL MATCH | TENNIS MATCHES | SYSTEMS | ONE-DAY CRICKET | RULES | Learning | Usage | Stochastic processes | Analysis | Studies | Operations research | Decision making models | Sports
Journal Article
Journal of Physics A: Mathematical and Theoretical, ISSN 1751-8113, 11/2016, Volume 49, Issue 49, p. 494002
We consider the inverse problem of reconstructing the posterior measure over the trajectories of a diffusion process from discrete time observations and... 
diffusion processes | Markov jump processes | expectation propagation algorithm | variational approximate inference | PHYSICS, MULTIDISCIPLINARY | KINETICS | INFERENCE | PHYSICS, MATHEMATICAL | Statistics - Machine Learning
Journal Article
IEEE Transactions on Signal Processing, ISSN 1053-587X, 02/2013, Volume 61, Issue 4, pp. 907 - 920
Journal Article
Journal of Engineering Mechanics, ISSN 0733-9399, 03/2015, Volume 141, Issue 3, p. 4014123
Journal Article
BMC Evolutionary Biology, ISSN 1471-2148, 2008, Volume 8, Issue 1, pp. 262 - 262
Journal Article
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, ISSN 1364-503X, 02/2013, Volume 371, Issue 1984, p. 20110543
Journal Article
1955, Wiley publications in statistics, 365
Book
Journal Article
Philosophical Transactions: Mathematical, Physical and Engineering Sciences, ISSN 1364-503X, 2/2013, Volume 371, Issue 1984, pp. 1 - 22
We present an unsupervised learning algorithm (GenESeSS) to infer the causal structure of quantized stochastic processes, defined as stochastic dynamical... 
Time series models | Probability distributions | Symbolism | Automata | Stochastic processes | Alphabets | Machine learning | Inference | Modeling | Vertices
Journal Article
Bayesian Analysis, ISSN 1936-0975, 2016, Volume 11, Issue 3, pp. 797 - 820
Journal Article