## Bayesian Reasoning and Machine LearningMachine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Other editions - View all

### Common terms and phrases

adjacency matrix algorithm approach approximation argmax assume Bayes Bayesian belief network binary classifier clique cluster components compute conditional independence Consider convergence corresponding covariance function datapoints dataset decision defined dependent derivative Dirichlet discrete dynamics eigenvalues eigenvectors EM algorithm Equation example expected utility exponential factor graph factorised Figure filtering Gaussian Gibbs sampling given gradient Hence hyperparameters inference influence diagram initialisation input iteration junction tree Kullback–Leibler divergence linear log likelihood M-step machine learning marginal likelihood Markov chain Markov network matrix maximise maximum likelihood mean method minimising mixture neighbours nodes observed optimal optimisation output parameters posterior potential prediction prior probabilistic probability problem procedure random recursion regression represent representation sample Section sequence singly connected solution straightforward structure term timestep training data transition triangulated undirected update variance vector weights zero