New York: Wiley-ISTE, 2017. — 290 p.
Оценка параметров и проверка гипотез - это основные инструменты
статистического вывода. Эти методы имеют место во многих
приложениях обработки данных, также методы Монте-Карло стали важным
инструментом для оценки производительности. Автор подразумевает
хорошие знания у читателя языка Python и понимание основ DSP. Для
учебных целей книга включает в себя несколько вычислительных задач
и упражнений. Чтобы предотвратить студентов от застревания на
упражнениях, приводятся подробные поправки.
This book addresses the basic principles of statistical inference.
The first chapter recalls the basis of probabilities. The second
chapter is devoted to point estimation, the hypothesis test,
including p-values, the confidence region determination, mentioning
only the most important concepts. The main practical approaches,
such as the least squares minimization, the moment method and the
likelihood maximization, are broadly detailed. The following
chapter is devoted to hidden Markov models (HMM) which are the
basis of the more recent algorithms in signal and image processing.
The final chapter is devoted to Monte-Carlo methods, which provide
useful estimation tools using well-chosen random generators. To
understand a theory well, it must be supported by practical
examples. Therefore, 16 computational examples and 62 computational
exercises using a real dataset are proposed. The solutions are
coded in Python language and provides in an appendix.
About the Author:
Maurice Charbit is Professor at Telecom ParisTech, France. He is a teacher in probability theory, signal processing, communication theory and statistics for data processing. With regard to research, his main areas of interest are: (i) the Bayesian approach for hidden Markov models, (ii) the 3D model-based approach for face tracking, and (iii) processing for multiple sensor arrays with applications to infrasonic systems. Useful Maths.
Statistical Inferences.
Inferences on HMM.
Monte-Carlo Methods.
Hints and Solutions.
Maurice Charbit is Professor at Telecom ParisTech, France. He is a teacher in probability theory, signal processing, communication theory and statistics for data processing. With regard to research, his main areas of interest are: (i) the Bayesian approach for hidden Markov models, (ii) the 3D model-based approach for face tracking, and (iii) processing for multiple sensor arrays with applications to infrasonic systems. Useful Maths.
Statistical Inferences.
Inferences on HMM.
Monte-Carlo Methods.
Hints and Solutions.