Background Mathematical modeling is often used to formalize hypotheses on how a
biochemical network operates by discriminating between competing models. Bayesian …

It is becoming increasingly important to develop novel signal processing and statistical
analysis techniques to extract information from biotechnology. This task is complicated by …

Abstract The Systems Biology community has taken numerous actions to develop data and
modeling standards towards FAIR data and model handling. Nevertheless, the debate about …

Motivation: High-throughput data are now commonplace in biological research. Rapidly
changing technologies and application mean that novel methods for detecting differential …

Approximate Bayesian computation (ABC) or likelihood-free inference algorithms are used
to find approximations to posterior distributions without making explicit use of the likelihood …

Motivation: Our goal is to construct a model for genetic regulatory networks such that the
model class:(i) incorporates rule-based dependencies between genes;(ii) allows the …

Probabilistic Methods for Financial and Marketing Informatics aims to provide students with
insights and a guide explaining how to apply probabilistic reasoning to business problems …

Bayesian statistics is now an established statistical methodology in almost all research
disciplines and is being applied to a very wide range of problems. These approaches are …

At the intersection between artificial intelligence and statistics, supervised learning allows
algorithms to automatically build predictive models from just observations of a system …

In this chapter, we discuss a number of approaches to network inference from large-scale
functional genomics data. Our goal is to describe current methods that can be used to infer …