Supplement to “A Comparative Review of Dimension Reduction Methods in Approximate
Bayesian Computation”. The supplement contains for each of the three examples a …

In the analysis and prediction of real-world systems, two of the key problems are
nonstationarity (often in the form of switching between regimes), and overfitting (particularly …

Natural language processing (NLP) and neural networks (NNs) have both undergone
significant changes in recent years. For active learning (AL) purposes, NNs are, however …

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex
problems for which the likelihood is either mathematically or computationally intractable …

This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Feedforward neural networks, particularly multilayer perceptrons, are widely used in
regression and classification tasks. A reliable and practical measure of prediction …

The chapter opens with an introduction to regression and its implementation within the
maximum-likelihood framework. This is followed by a general introduction to classical …

Neural network outputs are interpreted as parameters of statistical distributions. This allows
us to fit conditional distributions in which the parameters depend on the inputs to the …

We propose a two-phase systematical framework for approximation algorithm design and
analysis via Lyapunov function. The first phase consists of using Lyapunov function as an …

This brief proposes an efficient technique for the construction of optimized prediction
intervals (PIs) by using the bootstrap technique. The method employs an innovative PI …