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K16714
Developed from celebrated Harvard statistics lectures, Introduction to
Probability provides essential language and tools for understanding statistics,
randomness, and uncertainty. The book explores a wide variety of applications
and examples, ranging from coincidences and paradoxes to Google PageRank
and Markov chain Monte Carlo (MCMC). Additional application areas explored
include genetics, medicine, computer science, and information theory.
The authors present the material in an accessible style and motivate concepts
using real-world examples. Throughout, they use stories to uncover connections
between the fundamental distributions in statistics and conditioning to reduce
complicated problems to manageable pieces.
The book includes many intuitive explanations, diagrams, and practice problems.
Each chapter ends with a section showing how to perform relevant simulations
and calculations in R, a free statistical software environment.
Statistics
Texts in Statistical Science
Joseph K. Blitzstein
Jessica Hwang
Blitzstein • Hwang
Introduction to
Probability
Introduction to
Probability
Introduction to Probability
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Accessing the E-book edition of
INTRODUCTION TO PROBABILITY
Introduction to
Probability
K16714_FM.indd 1 6/11/14 2:36 PM
CHAPMAN & HALL/CRC
Texts in Statistical Science Series
Series Editors
Francesca Dominici, Harvard School of Public Health, USA
Julian J. Faraway, University of Bath, UK
Martin Tanner, Northwestern University, USA
Jim Zidek, University of British Columbia, Canada
Statistical eory: A Concise Introduction
F. Abramovich and Y. Ritov
Practical Multivariate Analysis, Fifth Edition
A. A, S. May, and V.A. Clark
Practical Statistics for Medical Research
D.G. Altman
Interpreting Data: A First Course
in Statistics
A.J.B. Anderson
Introduction to Probability with R
K. Baclawski
Linear Algebra and Matrix Analysis for
Statistics
S. Banerjee and A. Roy
Statistical Methods for SPC and TQM
D. Bissell
Introduction to Probability
J. K. Blitzstein and J. Hwang
Bayesian Methods for Data Analysis,
ird Edition
B.P. Carlin and T.A. Louis
Second Edition
R. Caulcutt
e Analysis of Time Series: An Introduction,
Sixth Edition
C. Chateld
Introduction to Multivariate Analysis
C. Chateld and A.J. Collins
Problem Solving: A Statistician’s Guide,
Second Edition
C. Chateld
Statistics for Technology: A Course in Applied
Statistics, ird Edition
C. Chateld
Bayesian Ideas and Data Analysis: An
Introduction for Scientists and Statisticians
R. Christensen, W. Johnson, A. Branscum,
and T.E. Hanson
Modelling Binary Data, Second Edition
D. Collett
Modelling Survival Data in Medical Research,
Second Edition
D. Collett
Introduction to Statistical Methods for
Clinical Trials
T.D. Cook and D.L. DeMets
Applied Statistics: Principles and Examples
D.R. Cox and E.J. Snell
Multivariate Survival Analysis and Competing
Risks
M. Crowder
Statistical Analysis of Reliability Data
M.J. Crowder, A.C. Kimber,
T.J. Sweeting, and R.L. Smith
An Introduction to Generalized
Linear Models, ird Edition
A.J. Dobson and A.G. Barnett
Nonlinear Time Series: eory, Methods, and
Applications with R Examples
R. Douc, E. Moulines, and D.S. Stoer
Introduction to Optimization Methods and
eir Applications in Statistics
B.S. Everitt
Extending the Linear Model with R:
Generalized Linear, Mixed Eects and
Nonparametric Regression Models
J.J. Faraway
Linear Models with R, Second Edition
J.J. Faraway
A Course in Large Sample eory
T.S. Ferguson
Multivariate Statistics: A Practical
Approach
B. Flury and H. Riedwyl
Readings in Decision Analysis
S. French
Markov Chain Monte Carlo:
Stochastic Simulation for Bayesian Inference,
Second Edition
D. Gamerman and H.F. Lopes
K16714_FM.indd 2 6/11/14 2:36 PM
Bayesian Data Analysis, ird Edition
A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson,
A. Vehtari, and D.B. Rubin
Multivariate Analysis of Variance and
Repeated Measures: A Practical Approach for
Behavioural Scientists
D.J. Hand and C.C. Taylor
Practical Data Analysis for Designed Practical
Longitudinal Data Analysis
D.J. Hand and M. Crowder
Logistic Regression Models
J.M. Hilbe
Richly Parameterized Linear Models:
Additive, Time Series, and Spatial Models
Using Random Eects
J.S. Hodges
Statistics for Epidemiology
N.P. Jewell
Stochastic Processes: An Introduction,
Second Edition
P.W. Jones and P. Smith
e eory of Linear Models
B. Jørgensen
Principles of Uncertainty
J.B. Kadane
Graphics for Statistics and Data Analysis with R
K.J. Keen
Mathematical Statistics
K. Knight
Introduction to Multivariate Analysis:
Linear and Nonlinear Modeling
S. Konishi
Nonparametric Methods in Statistics with SAS
Applications
O. Korosteleva
Modeling and Analysis of Stochastic Systems,
Second Edition
V.G. Kulkarni
Exercises and Solutions in Biostatistical eory
L.L. Kupper, B.H. Neelon, and S.M. O’Brien
Exercises and Solutions in Statistical eory
L.L. Kupper, B.H. Neelon, and S.M. O’Brien
Design and Analysis of Experiments with SAS
J. Lawson
A Course in Categorical Data Analysis
T. Leonard
Statistics for Accountants
S. Letchford
Introduction to the eory of Statistical
Inference
H. Liero and S. Zwanzig
Statistical eory, Fourth Edition
B.W. Lindgren
Stationary Stochastic Processes: eory and
Applications
G. Lindgren
e BUGS Book: A Practical Introduction to
Bayesian Analysis
D. Lunn, C. Jackson, N. Best, A. omas, and
D. Spiegelhalter
Introduction to General and Generalized
Linear Models
H. Madsen and P. yregod
Time Series Analysis
H. Madsen
Pólya Urn Models
H. Mahmoud
Randomization, Bootstrap and Monte Carlo
Methods in Biology, ird Edition
B.F.J. Manly
Introduction to Randomized Controlled
Clinical Trials, Second Edition
J.N.S. Matthews
Statistical Methods in Agriculture and
Experimental Biology, Second Edition
R. Mead, R.N. Curnow, and A.M. Hasted
Statistics in Engineering: A Practical Approach
A.V. Metcalfe
Beyond ANOVA: Basics of Applied Statistics
R.G. Miller, Jr.
A Primer on Linear Models
J.F. Monahan
Applied Stochastic Modelling, Second Edition
B.J.T. Morgan
Elements of Simulation
B.J.T. Morgan
Probability: Methods and Measurement
A. O’Hagan
Introduction to Statistical Limit eory
A.M. Polansky
Applied Bayesian Forecasting and Time Series
Analysis
A. Pole, M. West, and J. Harrison
Statistics in Research and Development,
Time Series: Modeling, Computation, and
Inference
R. Prado and M. West
K16714_FM.indd 3 6/11/14 2:36 PM
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