This book is meant to be a practical introduction into the use of probability and statistics in experimental physics for advanced undergraduate students and for graduate students. I have attempted to write a short book. It is not intended as a comprehensive text in probability and statistics. I have tried to emphasize areas I have found to be useful when doing experimental physics. Except for the first two chapters the emphasis is on applications and understanding. I have omitted proofs of formal theorems in the interests of brevity unless I felt the proof added to one's intuition in understanding and applying the theorem. Since, however, this is a field in which there are often a number of misunderstandings, it is necessary to state some things with reasonable precision. I have tried to do this when necessary. I assume the student is familiar with partial derivatives and with ele� mentary matrix manipulation. A computer is a needed tool for probability and statistics in experimental physics. We will introduce its use in this subject in some of the homework problems. One may interact with a computer in a batch mode or an inter� active mode. In a batch mode, one submits FORTRAN or other language programs, the computer processes them, and returns the end results. In the interactive mode, one gives the computer an instruction, the computer processes it, indicates what it has done, and waits for the next instruction.

Contenuti

Preface.- Basic Probability Concepts.- Some Initial Definitions.- Some Results Independent of Specific Distributions.- Discrete Distributions and Combinatorials.- Specific Discrete Distributions.- The Normal (or Gaussian) Distribution and Other Continuous Distributions.- Generating Functions and Characteristic Functions.- The Monte Carlo Method: Computer Simulation of Experiments.- Two Dimensional and Multi-Dimensional Distributions.- The Central Limit Theorem.- Inverse Probability; Confidence Limits.- Methods for Estimating Parameters. Least Squares and Maximum Likelihood.- Curve Fitting.- Bartlett S Function; Estimating Likelihood Ratios Needed for an Experiment.- Interpolating Functions and Unfolding Problems.- Fitting Data with Correlations and Constraints.- Beyond Maximum Likelihood and Least Squares; Robust Methods.- References. Index.

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