{"product_id":"9781784392055","title":"Learning Probabilistic Graphical Models in R","description":"\u003cp\u003e\u003cb\u003eFamiliarize yourself with probabilistic graphical models through realworld problems and illustrative code examples in R\u003c\/b\u003e\u003c\/p\u003e\u003cb\u003eAbout This Book\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003ePredict and use a probabilistic graphical models (PGM) as an expert system\u003c\/li\u003e\n\u003cli\u003eComprehend how your computer can learn Bayesian modeling to solve realworld problems\u003c\/li\u003e\n\u003cli\u003eKnow how to prepare data and feed the models by using the appropriate algorithms from the appropriate R package\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cp\u003eThis book is for anyone who has to deal with lots of data and draw conclusions from it, especially when the data is noisy or uncertain. Data scientists, machine learning enthusiasts, engineers, and those who curious about the latest advances in machine learning will find PGM interesting.\u003c\/p\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the concepts of PGM and which type of PGM to use for which problem\u003c\/li\u003e\n\u003cli\u003eTune the model's parameters and explore new models automatically\u003c\/li\u003e\n\u003cli\u003eUnderstand the basic principles of Bayesian models, from simple to advanced\u003c\/li\u003e\n\u003cli\u003eTransform the old linear regression model into a powerful probabilistic model\u003c\/li\u003e\n\u003cli\u003eUse standard industry models but with the power of PGM\u003c\/li\u003e\n\u003cli\u003eUnderstand the advanced models used throughout today's industry\u003c\/li\u003e\n\u003cli\u003eSee how to compute posterior distribution with exact and approximate inference algorithms\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eIn Detail\u003c\/b\u003e\u003cp\u003eProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graphbased representation. Two branches of graphical representations of distributions are commonly used, namely Bayesian networks and Markov networks. R has many packages to implement graphical models.\u003c\/p\u003e\u003cp\u003eWe'll start by showing you how to transform a classical statistical model into a modern PGM and then look at how to do exact inference in graphical models. Proceeding, we'll introduce you to many modern R packages that will help you to perform inference on the models. We will then run a Bayesian linear regression and you'll see the advantage of going probabilistic when you want to do prediction.\u003c\/p\u003e\u003cp\u003eNext, you'll master using R packages and implementing its techniques. Finally, you'll be presented with machine learning applications that have a direct impact in many fields. Here, we'll cover clustering and the discovery of hidden information in big data, as well as two important methods, PCA and ICA, to reduce the size of big problems.\u003c\/p\u003e\u003cb\u003eStyle and approach \u003c\/b\u003e\u003cp\u003eThis book gives you a detailed and stepbystep explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to realworld problems. The mathematics is kept simple and each formula is explained thoroughly.\u003c\/p\u003e","brand":"Packt Publishing, Limited","offers":[{"title":"Default Title","offer_id":47046532137200,"sku":"9781784392055","price":34.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781784392055_p0.jpg?v=1763726933","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781784392055","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}