Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Learning Bayesian Models with R

You're reading from  Learning Bayesian Models with R

Product type Book
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Pages 168 pages
Edition 1st Edition
Languages
Author (1):
Hari Manassery Koduvely Hari Manassery Koduvely
Profile icon Hari Manassery Koduvely

Table of Contents (16) Chapters

Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Introducing the Probability Theory The R Environment Introducing Bayesian Inference Machine Learning Using Bayesian Inference Bayesian Regression Models Bayesian Classification Models Bayesian Models for Unsupervised Learning Bayesian Neural Networks 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 has the following trivial factorization:
  2. The bivariate normal distribution is given by:

    Here:

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

  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
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}