Intermediate Probability Theory for Biomedical Engineers
Enderle, John D.
Intermediate Probability Theory for Biomedical Engineers by John Enderle, David Farden, Daniel Krause - 1st edition 2006 - 1 recurso en línea (VIII, 106 páginas) - Synthesis Lectures on Biomedical Engineering 1930-0336 .
This is the second in a series of three short books on probability theory and random processes for biomedical engineers. This volume focuses on expectation, standard deviation, moments, and the characteristic function. In addition, conditional expectation, conditional moments and the conditional characteristic function are also discussed. Jointly distributed random variables are described, along with joint expectation, joint moments, and the joint characteristic function. Convolution is also developed. A considerable effort has been made to develop the theory in a logical manner-developing special mathematical skills as needed. The mathematical background required of the reader is basic knowledge of differential calculus. Every effort has been made to be consistent with commonly used notation and terminology-both within the engineering community as well as the probability and statistics literature. The aim is to prepare students for the application of this theory to a wide variety of problems, as well give practicing engineers and researchers a tool to pursue these topics at a more advanced level. Pertinent biomedical engineering examples are used throughout the text.
9783031016141
10.1007/978-3-031-01614-1 doi
Probabilidades
Variables aleatorias
Biometría
QA273 / 2006 EB
Intermediate Probability Theory for Biomedical Engineers by John Enderle, David Farden, Daniel Krause - 1st edition 2006 - 1 recurso en línea (VIII, 106 páginas) - Synthesis Lectures on Biomedical Engineering 1930-0336 .
This is the second in a series of three short books on probability theory and random processes for biomedical engineers. This volume focuses on expectation, standard deviation, moments, and the characteristic function. In addition, conditional expectation, conditional moments and the conditional characteristic function are also discussed. Jointly distributed random variables are described, along with joint expectation, joint moments, and the joint characteristic function. Convolution is also developed. A considerable effort has been made to develop the theory in a logical manner-developing special mathematical skills as needed. The mathematical background required of the reader is basic knowledge of differential calculus. Every effort has been made to be consistent with commonly used notation and terminology-both within the engineering community as well as the probability and statistics literature. The aim is to prepare students for the application of this theory to a wide variety of problems, as well give practicing engineers and researchers a tool to pursue these topics at a more advanced level. Pertinent biomedical engineering examples are used throughout the text.
9783031016141
10.1007/978-3-031-01614-1 doi
Probabilidades
Variables aleatorias
Biometría
QA273 / 2006 EB