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Learning Bayesian Models with R

You're reading from   Learning Bayesian Models with R Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

Exercises

  1. By using the definition of conditional probability, show that any multivariate joint distribution of N random variables Exercises has the following trivial factorization:
    Exercises
  2. The bivariate normal distribution is given by:
    Exercises

    Here:

    Exercises

    By using the definition of conditional probability, show that the conditional distribution Exercises can be written as a normal distribution of the form Exercises where Exercises and Exercises.

  3. By using explicit integration of the expression in exercise 2, show that the marginalization of bivariate normal distribution will result in univariate normal distribution.
  4. In the following table, a dataset containing the measurements of petal and sepal sizes of 15 different Iris flowers are shown (taken from the Iris dataset, UCI machine learning dataset repository). All units are in cms:

    Sepal Length

    Sepal Width

    Petal Length

    Petal Width

    Class of Flower

    5.1

    3.5

    1.4

    0.2

    Iris-setosa

    4.9

    3

    1.4

    0.2

    Iris-setosa

    4.7

    3.2

    1.3

    0.2

    Iris-setosa

    4.6

    3.1

    1.5

    0.2

    Iris-setosa

    5

    3.6

    1.4

    0.2

    Iris-setosa

    7

    3.2

    4.7

    1.4

    Iris-versicolor

    6.4

    3.2

    4.5

    1.5

    Iris-versicolor

    6.9

    3.1

    4.9

    1.5

    Iris-versicolor

    5.5

    2.3

    4

    1.3

    Iris-versicolor

    6.5

    2.8

    4.6

    1.5

    Iris-versicolor

    6.3

    3.3

    6

    2.5

    Iris-virginica

    5.8

    2.7

    5.1

    1.9

    Iris-virginica

    7.1

    3

    5.9

    2.1

    Iris-virginica

    6.3

    2.9

    5.6

    1.8

    Iris-virginica

    6.5

    3

    5.8

    2.2

    Iris-virginica

    Answer the following questions:

    1. What is the probability of finding flowers with a sepal length more than 5 cm and a sepal width less than 3 cm?
    2. What is the probability of finding flowers with a petal length less than 1.5 cm; given that petal width is equal to 0.2 cm?
    3. What is the probability of finding flowers with a sepal length less than 6 cm and a petal width less than 1.5 cm; given that the class of the flower is Iris-versicolor?
You have been reading a chapter from
Learning Bayesian Models with R
Published in: Oct 2015
Publisher: Packt
ISBN-13: 9781783987603
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