{"product_id":"modeling-the-internet-and-the-web-probabilistic-methods-and-algorithms-wiley-series-in-probability-and-statistics-9780470849064","title":"Modeling the Internet and the Web Probabilistic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDespite its haphazard growth, the Web hides powerful underlying regularities -- from the organization of its links to the patterns found in its use by millions of users. Probabilistic modelling allows many of these regularities to be predicted on the basis of theoretical models based on statistical methodology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"…I congratulate the authors on a very well-researched and well-written publication.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2004, Vol. 46, No. 3)  \u003cp\u003e“…fascinating …I highly recommend this book…” (Short Book Reviews, August 2004)\u003c\/p\u003e \u003cp\u003e“…a very well-researched and well-written publication.” (Technometrics, August 2004) \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface.  \u003cp\u003e\u003cb\u003e1 Mathematical Background.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Probability and Learning from a Bayesian Perspective.\u003c\/p\u003e \u003cp\u003e1.2 Parameter Estimation from Data.\u003c\/p\u003e \u003cp\u003e1.3 Mixture Models and the Expectation Maximization Algorithm.\u003c\/p\u003e \u003cp\u003e1.4 Graphical Models.\u003c\/p\u003e \u003cp\u003e1.5 Classification.\u003c\/p\u003e \u003cp\u003e1.6 Clustering.\u003c\/p\u003e \u003cp\u003e1.7 Power-Law Distributions.\u003c\/p\u003e \u003cp\u003e1.8 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Basic WWW Technologies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Web Documents.\u003c\/p\u003e \u003cp\u003e2.2 Resource Identifiers: URI, URL, and URN.\u003c\/p\u003e \u003cp\u003e2.3 Protocols.\u003c\/p\u003e \u003cp\u003e2.4 Log Files.\u003c\/p\u003e \u003cp\u003e2.5 Search Engines.\u003c\/p\u003e \u003cp\u003e2.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Web Graphs.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Internet and Web Graphs.\u003c\/p\u003e \u003cp\u003e3.2 Generative Models for the Web Graph and Other Networks.\u003c\/p\u003e \u003cp\u003e3.3 Applications.\u003c\/p\u003e \u003cp\u003e3.4 Notes and Additional Technical References.\u003c\/p\u003e \u003cp\u003e3.5 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Text Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Indexing.\u003c\/p\u003e \u003cp\u003e4.2 Lexical Processing.\u003c\/p\u003e \u003cp\u003e4.3 Content-Based Ranking.\u003c\/p\u003e \u003cp\u003e4.4 Probabilistic Retrieval.\u003c\/p\u003e \u003cp\u003e4.5 Latent Semantic Analysis.\u003c\/p\u003e \u003cp\u003e4.6 Text Categorization.\u003c\/p\u003e \u003cp\u003e4.7 Exploiting Hyperlinks.\u003c\/p\u003e \u003cp\u003e4.8 Document Clustering.\u003c\/p\u003e \u003cp\u003e4.9 Information Extraction.\u003c\/p\u003e \u003cp\u003e4.10 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Link Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Early Approaches to Link Analysis.\u003c\/p\u003e \u003cp\u003e5.2 Nonnegative Matrices and Dominant Eigenvectors.\u003c\/p\u003e \u003cp\u003e5.3 Hubs and Authorities: HITS.\u003c\/p\u003e \u003cp\u003e5.4 PageRank.\u003c\/p\u003e \u003cp\u003e5.5 Stability.\u003c\/p\u003e \u003cp\u003e5.6 Probabilistic Link Analysis.\u003c\/p\u003e \u003cp\u003e5.7 Limitations of Link Analysis.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Advanced Crawling Techniques.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Selective Crawling.\u003c\/p\u003e \u003cp\u003e6.2 Focused Crawling.\u003c\/p\u003e \u003cp\u003e6.3 Distributed Crawling.\u003c\/p\u003e \u003cp\u003e6.4 Web Dynamics.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Modeling and Understanding Human Behavior on the\u003c\/b\u003e \u003cb\u003eWeb.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Web Data and Measurement Issues.\u003c\/p\u003e \u003cp\u003e7.3 Empirical Client-Side Studies of Browsing Behavior.\u003c\/p\u003e \u003cp\u003e7.4 Probabilistic Models of Browsing Behavior.\u003c\/p\u003e \u003cp\u003e7.5 Modeling and Understanding Search Engine Querying.\u003c\/p\u003e \u003cp\u003e7.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Commerce on the Web: Models and Applications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Customer Data on theWeb.\u003c\/p\u003e \u003cp\u003e8.3 Automated Recommender Systems.\u003c\/p\u003e \u003cp\u003e8.4 Networks and Recommendations.\u003c\/p\u003e \u003cp\u003e8.5 Web Path Analysis for Purchase Prediction.\u003c\/p\u003e \u003cp\u003e8.6 Exercises.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Mathematical Complements.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Graph Theory.\u003c\/p\u003e \u003cp\u003eA.2 Distributions.\u003c\/p\u003e \u003cp\u003eA.3 Singular Value Decomposition.\u003c\/p\u003e \u003cp\u003eA.4 Markov Chains.\u003c\/p\u003e \u003cp\u003eA.5 Information Theory.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: List of Main Symbols and Abbreviations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402440384855,"sku":"9780470849064","price":77.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470849064.jpg?v=1730480402","url":"https:\/\/bookcurl.com\/products\/modeling-the-internet-and-the-web-probabilistic-methods-and-algorithms-wiley-series-in-probability-and-statistics-9780470849064","provider":"Book Curl","version":"1.0","type":"link"}