Artboard 33 Artboard 16 Artboard 18 Artboard 15 Artboard 21 Artboard 1 Artboard 2 Artboard 5 Artboard 45 Artboard 45 Artboard 22 Artboard 9 Artboard 23 Artboard 17? Artboard 28 Artboard 43 Artboard 49 Artboard 47 Artboard 38 Artboard 32 Artboard 8 Artboard 22 Artboard 5 Artboard 25 Artboard 1 Artboard 42 Artboard 11 Artboard 41 Artboard 13 Artboard 23 Artboard 10 Artboard 4 Artboard 9 Artboard 20 Artboard 6 Artboard 11 Artboard 7 Artboard 3 Artboard 3 Artboard 12 Artboard 25 Artboard 34 Artboard 39 Artboard 24 Artboard 13 Artboard 19 Artboard 7 Artboard 24 Artboard 31 Artboard 4 Artboard 14 Artboard 27 Artboard 30 Artboard 36 Artboard 44 Artboard 12 Artboard 17 Artboard 17 Artboard 6 Artboard 27 Artboard 19 Artboard 30 Artboard 29 Artboard 29 Artboard 26 Artboard 18 Artboard 2 Artboard 20 Artboard 35 Artboard 15 Artboard 14 Artboard 48 Artboard 50 Artboard 26 Artboard 16 Artboard 40 Artboard 21 Artboard 29 Artboard 10 Artboard 37 Artboard 3 Artboard 3 Artboard 46 Artboard 8
e-book

Learning Probabilistic Graphical Models in R (Cód: 9477339)

David Bellot

Packt Publishing

Ooops! Este produto não está mais a venda.
Mas não se preocupe, temos uma versão atualizada para você.

Ooopss! Este produto está fora de linha, mas temos outras opções para você.
Veja nossas sugestões abaixo!

R$ 89,97 em até 2x de R$ 44,99 sem juros
Cartão Saraiva R$ 89,97 ou em até 4x de R$ 22,49 sem juros

Crédito:
Boleto:
Cartão Saraiva:

Total: R$0,00

Em até 2x sem juros de R$ 0,00


Learning Probabilistic Graphical Models in R

R$89,97

Descrição

Familiarize yourself with probabilistic graphical models through real-world problems and illustrative code examples in RAbout This Book• Predict and use a probabilistic graphical models (PGM) as an expert system• Comprehend how your computer can learn Bayesian modeling to solve real-world problems• Know how to prepare data and feed the models by using the appropriate algorithms from the appropriate R packageWho This Book Is ForThis 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.What You Will Learn• Understand the concepts of PGM and which type of PGM to use for which problem• Tune the model's parameters and explore new models automatically• Understand the basic principles of Bayesian models, from simple to advanced• Transform the old linear regression model into a powerful probabilistic model• Use standard industry models but with the power of PGM• Understand the advanced models used throughout today's industry• See how to compute posterior distribution with exact and approximate inference algorithmsIn DetailProbabilistic graphical models (PGM, also known as graphical models) are a marriage between probability theory and graph theory. Generally, PGMs use a graph-based 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.We'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.Next, 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.Style and approach This book gives you a detailed and step-by-step explanation of each mathematical concept, which will help you build and analyze your own machine learning models and apply them to real-world problems. The mathematics is kept simple and each formula is explained thoroughly.

Características

Peso 0.00 Kg
Produto sob encomenda Sim
Marca Packt Publishing
Número de Páginas 250 (aproximado)
Idioma 337
Acabamento e-book
Territorialidade Internacional
Gratuito Não
Proteção Drm Sim
Tamanho do Arquivo 7381
Código do Formato Epub
Cód. Barras 9781784397418
Ano da Publicação 116
AutorDavid Bellot