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Comment: Paperback with remainder marks, in good condition. Text is clean, plastic laminate on back cover is peeling slightly at side edge.

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Introduction to Statistical Decision Theory (MIT Press) Paperback – January 25, 2008

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Product Details

  • Series: MIT Press
  • Paperback: 896 pages
  • Publisher: The MIT Press (January 25, 2008)
  • Language: English
  • ISBN-10: 026266206X
  • ISBN-13: 978-0262662062
  • Product Dimensions: 7 x 1.5 x 10 inches
  • Shipping Weight: 3.3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #1,148,480 in Books (See Top 100 in Books)

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Format: Hardcover Verified Purchase
This is a very accessible introduction to Bayesian statistics and its applications in economics and management science. The book would make an interesting and useful two semester course at the sophomore or junior level. The mathematical prerequisites are small - some calculus, but the book manages to introduce basic probability, stochastic processes, statistical inference, large sample theory, the multivariate normal distribution, and topics related to economics such as utility functions, lotteries, decision trees and portfolio theory. These topics are useful to students in a wide variety of disciplines: economics, finance, computer science, electrical engineering, statistics, and students in the sciences which may end up in the bio/health sciences (medial decision making). In addition with the growing prevalence of Bayesian computation across disciplines, it seems like a lost opportunity not to offer such a course.
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Format: Paperback Verified Purchase
This is one of those "introduction" books that actually a dictionary/encyclopedia on the subject, but as its written by experts in the field - who took years to do so- well worth a place on your statistical bookshelf; hope you did well in Calculus.
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Format: Paperback
Ordered the book for my boss. He likes the book. I am going to quit rating books because you have to many rules.
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