{"product_id":"probabilistic-numerics-computation-as-machine-learning-9781107163447","title":"Probabilistic Numerics: Computation as Machine Learning","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eProbabilistic numerical computation connects machine learning and applied mathematics, approximating intractable quantities from computable ones using numerical algorithms. This book demonstrates how computational routines can be viewed as learning machines and Bayesian inference can be used to build more flexible and efficient algorithms. It is suitable for Masters and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 410 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 30 June 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Cambridge University Press\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eProbabilistic numerical computation is a powerful tool that formalises the connection between machine learning and applied mathematics. It involves the use of numerical algorithms to approximate intractable quantities from computable ones, such as estimating integrals from evaluations of the integrand or the path of a dynamical system described by differential equations from evaluations of the vector field. These algorithms infer a latent quantity from data, making it possible to think of computational routines as learning machines.\u003cbr\u003e\u003cbr\u003eThis book demonstrates that it is formally possible to think of computational routines as learning machines and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. It caters to Masters and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided, along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.\u003cbr\u003e\u003cbr\u003eBy leveraging probabilistic numerical computation, researchers can develop more accurate models, make informed decisions, and solve complex problems in a wide range of fields. This book provides a comprehensive introduction to the topic and is an essential resource for anyone interested in advancing their knowledge in this area.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 1160g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 211 x 259 x 25 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781107163447\u003c\/p\u003e","brand":"PhilippHennig,Michael A.Osborne,Hans P.Kersting","offers":[{"title":"Hardback","offer_id":44094891852026,"sku":"9781107163447","price":58.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1657294469568_book.jpg?v=1657776109","url":"https:\/\/shulphink.com\/products\/probabilistic-numerics-computation-as-machine-learning-9781107163447","provider":"Shulph Ink","version":"1.0","type":"link"}