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Journal of Machine Learning Research, ISSN 1532-4435, 11/2010, Volume 11, pp. 3011 - 3015
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions;... 
Probabilistic regression and classification | Nonparametric bayes | Gaussian processes | nonparametric Bayes | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | probabilistic regression and classification
Journal Article
New Journal of Physics, ISSN 1367-2630, 09/2016, Volume 18, Issue 9, p. 93020
The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A... 
general probabilistic theories | dynamical causal order | process matrix framework | indefinite causal order | quantum foundations | causality | PHYSICS, MULTIDISCIPLINARY | Causality | Quantum physics | Quantum mechanics | Physics - Quantum Physics
Journal Article
International Journal of Computer Vision, ISSN 0920-5691, 6/2010, Volume 88, Issue 2, pp. 169 - 188
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 02/2015, Volume 37, Issue 2, pp. 408 - 423
Journal Article
Journal of Computational Physics, ISSN 0021-9991, 11/2017, Volume 348, pp. 683 - 693
This work leverages recent advances in probabilistic machine learning to discover governing equations expressed by parametric linear operators. Such equations... 
Uncertainty quantification | Inverse problems | Fractional differential equations | Functional genomics | Probabilistic machine learning | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | MODELS | PHYSICS, MATHEMATICAL | Analysis | Differential equations | Gaussian processes | Machine learning | Mechanical engineering
Journal Article
2013, De Gruyter studies in mathematics, ISBN 9783110302004, Volume 48, x, 284
Book
Journal of Machine Learning Research, ISSN 1532-4435, 06/2010, Volume 11, pp. 1865 - 1881
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a... 
Gaussian process | Sparse approximation | Computational efficiency | Probabilistic regression | Power spectrum | power spectrum | sparse approximation | computational efficiency | probabilistic regression | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Industrial and Engineering Chemistry Research, ISSN 0888-5885, 05/2015, Volume 54, Issue 18, pp. 5037 - 5047
Prediction uncertainty has rarely been integrated into traditional soft sensors in industrial processes. In this work, a novel autoswitch probabilistic soft... 
ENGINEERING, CHEMICAL | QUALITY PREDICTION | BATCH PROCESSES | NEURAL-NETWORKS | POLYMERIZATION REACTORS | SUPPORT VECTOR REGRESSION | NONLINEAR-SYSTEMS | MELT INDEX PREDICTION | PARTIAL LEAST-SQUARES | MODEL | IDENTIFICATION | Quality | Probability theory | Regression | Mathematical models | Gaussian | Probabilistic methods | Sensors | Online
Journal Article
Journal of Applied Polymer Science, ISSN 0021-8995, 02/2015, Volume 132, Issue 6, pp. np - n/a
ABSTRACT In internal rubber‐mixing processes, data‐driven soft sensors have become increasingly important for providing online measurements for the Mooney... 
manufacturing | rubber | applications | MACHINES | POLYMER SCIENCE | MOONEY-VISCOSITY | SOFT SENSORS | SUPPORT VECTOR REGRESSION | MELT INDEX PREDICTION | NEURAL-NETWORK | LEAST-SQUARES | Models | Rubber | Analysis | Rubber industry | Production processes | Gaussian processes | Viscosity | Uncertainty | Probability theory | Regression | Mathematical models | Gaussian | Probabilistic methods | Real time
Journal Article
Journal of the ACM (JACM), ISSN 0004-5411, 01/2014, Volume 61, Issue 1, pp. 1 - 45
Journal Article
Theoretical Computer Science, ISSN 0304-3975, 2004, Volume 318, Issue 3, pp. 323 - 354
The notion of process equivalence of probabilistic processes is sensitive to the exact probabilities of transitions. Thus, a slight change in the transition... 
Process Algebra | Metric | Labelled Markov processes | BISIMULATION | AUTOMATA | labelled Markov processes | process algebra | metric | PROBABILISTIC PROCESSES | COMPUTER SCIENCE, THEORY & METHODS
Journal Article
Current Opinion in Neurobiology, ISSN 0959-4388, 2013, Volume 25, pp. 38 - 46
Highlights • The brain can optimally integrate information across sensory systems. • Probabilistic population codes solve many problems in multisensory cue... 
Neurology | Psychiatry | VENTRAL INTRAPARIETAL AREA | PERCEPTUAL DECISION | DECISION-MAKING | INTEGRATION | CAUSAL INFERENCE | PROBABILISTIC POPULATION CODES | SPATIAL REFERENCE FRAMES | REPRESENTATION | PARIETAL CORTEX | MACAQUE VISUAL-CORTEX | NEUROSCIENCES | Cues | Animals | Sensation - physiology | Humans | Neurons - physiology | Models, Neurological | Brain - physiology
Journal Article
IEEE Transactions on Automatic Control, ISSN 0018-9286, 09/2016, Volume 61, Issue 9, pp. 2538 - 2543
Journal Article
Technometrics, ISSN 0040-1706, 07/2014, Volume 56, Issue 3, pp. 302 - 311
Journal Article
IEEE Transactions on Automatic Control, ISSN 0018-9286, 09/2018, Volume 63, Issue 9, pp. 2844 - 2854
Computation of moments of transformed random variables is a problem appearing in many engineering applications. The current methods for moment transformation... 
Bayesian quadrature | function approximation | Transforms | Gaussian processes | moment transformation | recursive estimation | Probabilistic logic | Bayes methods | Random variables | Kalman filters | Kernel | FILTERS | AUTOMATION & CONTROL SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Coordinate transformations | Gaussian process | Filtration | Integrals | Bayesian analysis
Journal Article