Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. Ordinary Differential Equations 257 16.1 Basic theory of ODEs 257 16.2 Existence and uniqueness of solutions 258 We will describe the difference between direct sampling and Markov-chain sampling, and also study the connection of Monte Carlo and Molecular Dynamics algorithms, that is, the interface between Newtonian mechanics and statistical mechanics. diagnostics Article Statistical Physics for Medical Diagnostics: Learning, Inference, and Optimization Algorithms Abolfazl Ramezanpour 1,2, Andrew L. Beam 3,4,5, Jonathan H. Chen 6,7 and Alireza Mashaghi 1,* 1 Leiden Academic Centre for Drug Research, Faculty of Mathematics and Natural Sciences, Leiden University, 2333CC Leiden, The Netherlands; a.ramezanpour@lacdr.leidenuniv.nl This paper describes the completed work on classification in the StatLog project. 4, 477–505 DOI: 10.1214/07-STS242 c Institute of Mathematical Statistics, 2007 Boosting Algorithms: Regularization, Prediction and Model Fitting Peter Buhlmann and Torsten Hothorn Abstract. In Week 2, you will get in touch with the hard-disk model, which was first simulated by Molecular Dynamics in the 1950's. 22, No. Eigenvalue Algorithms 241 15.1 Power method 241 15.2 Inverse iteration 250 15.3 Singular value decomposition 252 15.4 Comparing factorizations 253 15.5 More reading 254 15.6 Exercises 254 15.7 Solutions 256 Chapter 16. We present a statistical perspective on boosting. The aim of the Stat Log project is to compare the performance of statistical, machine learning, and neural network algorithms, on large real world problems. Statistical algorithms for models in state space 117 1880 1900 1920 1940 1960 Nile 1950 1955 1960 200 300 400 500 600 airline 500 750 1000 1250 Figure 1. Statistical Science 2007, Vol. "An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Nile and airline data. Because machine learning is a branch of statistics, machine learning algorithms technically fall under statistical knowledge, as well as data mining and more computer-science-based methods. vi Contents II Sorting and Order Statistics Introduction 147 6Heapsort151 6.1 Heaps 151 6.2 Maintaining the heap property 154 6.3 Building a heap 156 6.4 The heapsort algorithm 159 6.5 Priority queues 162 7 Quicksort 170 7.1 Description of quicksort 170 7.2 Performance of quicksort 174 7.3 A randomized version of quicksort 179 7.4 Analysis of quicksort 180 8 Sorting in Linear Time 191 Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang 1John C. Duchi Martin Wainwright,2 1Department of Electrical Engineering and Computer Science and 2Department of Statistics University of California, Berkeley Berkeley, CA 94720 {yuczhang,jduchi,wainwrig}@eecs.berkeley.edu Chapter 15. Part I Classic Statistical Inference 1 1 Algorithms and Inference 3 1.1 A Regression Example 4 1.2 Hypothesis Testing 8 1.3 Notes 11 2 Frequentist Inference 12 2.1 Frequentism in Practice 14 2.2 Frequentist Optimality 18 2.3 Notes and Details 20 3 Bayesian Inference 22 3.1 Two Examples 24