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Statistical Decision Theory and Bayesian Analysis

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TLDR
An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
Abstract
1. Basic concepts 2. Utility and loss 3. Prior information and subjective probability 4. Bayesian analysis 5. Minimax analysis 6. Invariance 7. Preposterior and sequential analysis 8. Complete and essentially complete classes Appendices.

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Patent

Depth from time-of-flight using machine learning

TL;DR: In this article, a depth detection system with a trained machine learning component has been described, which is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained ML component.
Journal ArticleDOI

E-Bayesian estimation of failure probability and its application

TL;DR: This paper develops a new method, named E-Bayesian estimation method, to estimate failure probability, and applies it to the simulation example and application example; it is both efficient and easy to operate.
Journal ArticleDOI

Bayesian optimal design of an avalanche dam using a multivariate numerical avalanche model

TL;DR: A multivariate numerical avalanche propagation model within a Bayesian decisional framework, where the influence of a vertical dam on an avalanche flow is quantified in terms of local energy dissipation with a simple semi-empirical relation.
Journal ArticleDOI

DNA sequence classification via an expectation maximization algorithm and neural networks: a case study

TL;DR: An expectation maximization (EM) algorithm is used to locate the -35 and -10 binding sites in an E. Coli promoter sequence and deduces the probability distribution for these lengths, which is then fed to a neural network for promoter recognition.
Journal ArticleDOI

Maximum-Entropy and Bayesian Spectral Analysis and Estimation Problems

L. Mark Berliner
- 01 May 1989 - 
TL;DR: One of the books that can be recommended for new readers is maximum entropy and bayesian spectral analysis and estimation problems, which is not kind of difficult book to read.