Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

Build and operate multi-GNSS and multi-frequency receivers with state-of-the-art techniques using this up-to-date, thorough and easy-to-follow text. Covering both theory and practice and complemented by MATLAB© code and digital samples with which to test it, this package is a powerful learning tool for students, engineers and researchers everywhere. Suggestions of hardware equipment allow you to get to work straight away and to create your own samples. Concisely but clearly explaining all the fundamental concepts in one place, this is also a perfect resource for readers seeking an introduction to the topic.

In this chapter, we consider inference and learning problems that can be formulated as the optimization of an information-theoretic measure (ITM).

As introduced in Chapter 4, setting up a learning problem requires the selection of an inductive bias, which consists of a model class $\mathscr{H}$ and a training algorithm. By the no-free-lunch theorem, this first step is essential in order to make generalization possible. A trained model generalizes if it performs well outside the training set, on average with respect to the unknown population distribution.

As discussed in Chapter 2, learning is needed when a “physics”-based mathematical model for the data generation mechanism is not available or is too complex to use for design purposes. As an essential benchmark setting, this chapter discusses the ideal case in which an accurate mathematical model is known, and hence learning is not necessary. As in large part of machine learning, we specifically focus on the problem of prediction. The goal is to predict a target variable given the observation of an input variable based on a mathematical model that describes the joint generation of both variables. Model-based prediction is also known as inference.

Previous chapters have formulated learning problems within a frequentist framework. Frequentist learning aims to determine a value of the model parameter $\theta$ that approximately minimizes the population loss. Since the population loss is not known, this is in practice done by minimizing an estimate of the population loss $L_p(\theta )$ based on training data – the training loss $L_{𝒟}(\theta )$.