• Title: Bayesian Spectrum Analysis and Parameter Estimation
• Author(s) G. Larry Bretthorst
• Publisher: Springer; 1988th edition (November 28, 1988)
• Permission: Online Edition Provided by the Author.
• Paperback: 221 pages
• eBook: PDF (220 pages)
• Language: English
• ISBN-10: 0387968717
• ISBN-13: 978-0387968711
• Share This:  

Book Description

This work is primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material.

• N/A

• Amazon

• Bayesian Thinking
• Statistics, Mathematical Statistics
• Probability and Stochastic Processes
• Digital Signal Processing (DSP), Sound and Imaging Processing

• Bayesian Spectrum Analysis and Parameter Estimation (G. Larry Bretthorst)
• The Mirror Site (1) - PDF

• Bayesian Models of Perception and Action: An Introduction

  An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action.

• An Introduction to Bayesian Thinking (Merlise Clyde, et al.)

  It provides an introduction to Bayesian Inference in decision making without requiring calculus. It may be used on its own as an open-access introduction to Bayesian inference using R Programming for anyone interested in learning about Bayesian statistics.

• Bayes Rules! An Introduction to Applied Bayesian Modeling

  An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, it brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. Integrates R code, including RStan modeling tools, bayesrules package.

• O'Reilly® Think Bayes: Bayesian Statistics in Python

  If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

• Bayesian Data Analysis (Andrew Gelman, et al.)

  This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

• Bayesian Methods for Hackers: Probabilistic Programming

  This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.

• Bayesian Reasoning and Machine Learning (David Barber)

  This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

• Kalman and Bayesian Filters in Python (Roger R Labbe Jr.)

  This book is an introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn?

• Bayesian Methods for Statistical Analysis (Borek Puza)

  Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. It contains many exercises, all with worked solutions, including complete computer code.

• Bayesian Computational Methods (Christian P. Robert)

  This monograpg presents the most standard computational challenges met in Bayesian Statistics, focussing primarily on mixture estimation and on model choice issues, and then relate these problems with computational solutions.