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• Mathematical analysis
• Functional analysis
• Metaphysics
• Stochastic processes
• Distribution
• Stochastic simulation
• Dynamical system

Approximation of Probabilistic Reachability for Chemical Reaction Networks using the Linear Noise Approximation∗ Luca Bortolussi3 , Luca Cardelli1,2 , Marta Kwiatkowska2 , and Luca Laurenti2 1

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• Statistical randomness
• Mathematical analysis
• Probability theory
• Stochastic processes
• Markov models
• Graph theory
• Markov chain
• Stochastic differential equations
• Distribution
• Stochastic simulation
• Normal distribution
• Decomposition of spectrum

NOISE ROBUST SPEECH RECOGNITION USING GAUSSIAN BASIS FUNCTIONS FOR NON-LINEAR LIKELIHOOD FUNCTION APPROXIMATION Chris Pal½ ¾ , Brendan Frey½ ¾ and Trausti Kristjansson ½ ¾ ½ ¾

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Approximating the Diameter of Planar Graphs in Near Linear Time OREN WEIMANN and RAPHAEL YUSTER, University of Haifa We present a (1 + ε)-approximation algorithm running in O( f (ε) · n log4 n) time for finding the di

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Introduction Outline of A.E. Applications FBSDE Approximation Scheme Perturbation Technique for Non-linear FBSDEs with Interacting Particle Method Reference . . . . . . . . . . . .. .. .. .. .. .. .. . . . . . . . . . .

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9 Approximation for Multi-dimensional and Non-linear Preferences Approximation for multi-dimensional and non-linear agents is more urgent than for single-dimensional linear agents. For single-dimensional linear agents, C

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princeton univ. F’14 cos 521: Advanced Algorithm Design Lecture 7: Provable Approximation via Linear Programming Lecturer: Sanjeev Arora

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Linear Programming Based Approximation Algorithms for Feedback Set Problems in Bipartite Tournaments Anke van Zuylen Institute for Theoretical Computer Science, Tsinghua University, Beijing, China

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Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation Richard S. Sutton,∗ Hamid Reza Maei,∗ Doina Precup,† Shalabh Bhatnagar,‡ David Silver,∗ Csaba Szepesv´ari,∗ E

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