{"product_id":"9781680832181","title":"Patterns of Scalable Bayesian Inference","description":"\u003cp\u003eDatasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with a wide range of assumptions and applicability.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003ePatterns of Scalable Bayesian Inference\u003c\/em\u003e seeks to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. It examines how these techniques can be \u003cem\u003escaled up\u003c\/em\u003e to larger problems and \u003cem\u003escaled out\u003c\/em\u003e across parallel computational resources. It reviews existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, it characterizes the general principles that have proven successful for designing scalable inference procedures and addresses some of the significant open questions and challenges.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Now Publishers","offers":[{"title":"Default Title","offer_id":47055740141808,"sku":"9781680832181","price":95.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781680832181_p0.jpg?v=1763690664","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781680832181","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}