Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Bayesian statistical decision theory. --- Physical sciences --- Statistische data-analyse. --- Statistical methods. --- Mathematica (Computer file). --- Science --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mathematica (Computer file)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform. Deyi Xiong is a professor at Soochow University. Previously he was a research scientist at the Institute for Infocomm Research of Singapore from 2007-2013. He completed his Ph.D. in Computer Science at the Institute of Computing Technology of Chinese Academy of Sciences in 2007. His research interests are in the area of natural language processing, including parsing and statistical machine translation. Min Zhang is a professor at Soochow University. He obtained his Ph.D. degree in Computer Science at Harbin Institute of Technology in 1997. His research interests include machine translation, natural language processing and text mining.

Linguistics. --- Computational Linguistics. --- Computational linguistics. --- Linguistique --- Linguistique informatique --- COMPUTERS / General. --- Machine translating. --- Languages & Literatures --- Philology & Linguistics --- Statistische data-analyse --- Statistische linguïstiek --- Algoritmen --- Computers --- General --- General. --- Statistische data-analyse. --- Statistische linguïstiek. --- Algoritmen. --- Automatic translating --- Computer translating --- Electronic translating --- Mechanical translating --- Automatic language processing --- Language and languages --- Language data processing --- Linguistics --- Natural language processing (Linguistics) --- Data processing --- Algorithms --- Applied linguistics --- Artificial intelligence --- Natural language generation (Computer science) --- Information theory --- Translating and interpreting --- Cross-language information retrieval --- Translating machines --- Mathematical linguistics --- Multilingual computing