This is primarily a list of Greatest Mathematicians of the Past, but I use birth as an arbitrary cutoff, and two of the "Top " are still alive now. Click here for a longer List of including many more 20th-century mathematicians. Click for a discussion of certain omissions.

They can be used for non-linear regression, time-series modelling, classification, and many other problems. Streaming sparse Gaussian process approximations. Sparse approximations for Gaussian process models provide a suite of methods that enable these models to be deployed in large data regime and enable analytic intractabilities to be sidestepped.

However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive.

This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing principled methods for learning hyperparameters and optimising pseudo-input locations.

The proposed framework is experimentally validated using synthetic and real-world datasets.

The first two authors contributed equally. The unreasonable effectiveness of structured random orthogonal embeddings. We examine Gauss markov theorem class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation.

We introduce matrices with complex entries which give significant further accuracy improvement. We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

We present a data-efficient reinforcement learning method for continuous state-action systems under significant observation noise.

Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s. PILCO evaluates policies by planning state-trajectories using a dynamics model.

This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original unfiltered PILCO algorithm.

We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control.

Skoglund, Zoran Sjanic, and Manon Kok. On orientation estimation using iterative methods in Euclidean space. This paper presents three iterative methods for orientation estimation. The third method is based on nonlinear least squares NLS estimation of the angular velocity which is used to parametrise the orientation.

The Multivariate Generalised von Mises distribution: Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain.

First we introduce a new multivariate distribution over circular variables, called the multivariate Generalised von Mises mGvM distribution. This distribution can be constructed by restricting and renormalising a general multivariate Gaussian distribution to the unit hyper-torus.

Previously proposed multivariate circular distributions are shown to be special cases of this construction. Second, we introduce a new probabilistic model for circular regression, that is inspired by Gaussian Processes, and a method for probabilistic principal component analysis with circular hidden variables.

These models can leverage standard modelling tools e.In his doctorate in absentia, A new proof of the theorem that every integral rational algebraic function of one variable can be resolved into real factors of the first or second degree, Gauss proved the fundamental theorem of algebra which states that every non-constant single-variable polynomial with complex coefficients has at least one complex .

PatrickJMT: making FREE and hopefully useful math videos for the world! Andrey Andreyevich Markov, (born June 14, , Ryazan, Russia—died July 20, , Petrograd [now St.

Petersburg]), Russian mathematician who helped to develop the theory of stochastic processes, especially those called Markov plombier-nemours.com on the study of the probability of mutually dependent events, his work has been developed and widely applied in the biological and social sciences.

Gaussian Processes and Kernel Methods Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions.

They can be used for non-linear regression, time-series . Box and Cox () developed the transformation. Estimation of any Box-Cox parameters is by maximum likelihood. Box and Cox () offered an example in which the data had the form of survival times but the underlying biological structure was of hazard rates, and the transformation identified this.

Johann Carl Friedrich Gauss (April 30, – February 23, ) was a German mathematician and scientist of profound genius who contributed significantly to many fields, including number theory, analysis, differential geometry, geodesy, magnetism, astronomy, and plombier-nemours.com is particularly known for the unit of magnetism that bears his name, and by a mathematical expression (Gauss's Law) that.

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Gauss–Markov theorem - Wikipedia