Probability Theory: The Logic of Science is a book explaining the theory written by Edwin Thompson Jaynes. The book talks about how the principles of probability theory showcase the logic that science is so fond of.  The book was published by Cambridge University Press in 2003.

## Probability Theory: The Logic of Science Pdf Review:

The book is not the same mathematical explanation of the probability theory like some other books on the subject are. It tells more than that as it has information about the applications of this theory  The author explains how the rules of this theory can be interpreted in a mere logical sense. By doing so, the practical applications of this theory in many fields is made probably. Some of the fields mentioned in the book are Math, Physics, Chemistry, and Biology. Along with general information, there are also problem-solving exercises in the book for the readers to solve.

## Probability Theory: The Logic of Science Pdf Features:

• The book can be used as a textbook since it has exercises for the reader to solve.
• The book is for those people who are already familiar with mathematical principles and have at least graduate level of knowledge of math.
• It may not be suitable for general readers since there are some complex terms and explanations.
• This book would be of great advantage to those people who often need to collect data from incomplete information.

• 1 Plausible Reasoning
• 2 The Quantitative Rules
• 3 Elementary Sampling Theory
• Figure 3-1
• 4 Elementary Hypothesis Testing
• Figure 4-1
• 5 Queer Uses For Probability Theory
• 6 Elementary Parameter Estimation
• Figure 6-1
• Figure 6-2
• 7 The Central Gaussian, Or Normal, Distribution
• 8 Sufficiency, Ancillary, And All That
• 9 Repetitive Experiments – Probability and Frequency
• 10 Physics Of “Random Experiments”
• 11 Discrete Prior Probabilities – The Entropy Principle
• 13 Decision Theory – Historical Background
• 14 Simple Applications Of Decision Theory
• 15 Paradoxes Of Probability Theory
• Figure 15-1
• 16 Orthodox Methods: Historical Background
• 17 Principles and Pathology of Orthodox Statistics
• 18 The Ap Distribution And Rule Of Succession
• 19 Physical Measurements
• 20 Trend and Seasonality In Time Series
• 21 Regression And Linear Models
• 24 Model Comparison
• 27 Introduction To Communication Theory
• 30 Maximum Entropy: Matrix Formulation
• References
• Annexes
• The Other Approaches To Probability Theory
• B Mathematical Formalities And Style
• C Convolutions And Cumulants
• E Multivariate Gaussian Integrals