Scientific reasoning in art
Evaluating evidence in paintings research
To help clarify complex situations where the relative weight of evidence with respect to a given hypothesis needs to be established, it is possible to use Bayesian networks, probabilistic mathematical models. These networks can accommodate a large variation in data and can quantify the value or weight of each piece of evidence and how it influences a hypothesis in relation to other evidence. The networks are flexible and allow the incorporation of new evidence and thus quantify its influence on the overall chain of evidence. This project will apply Bayesian networks to questions regarding authentication of paintings, since these questions combine traditional humanities-based methods with scientific investigation using instrumental analytis. Keeping an overview of information delivered by different specialists and establishing its relative weight is a growing challenge. Bayesian frameworks will sharpen research methodologies by pointing to possible weak areas and will help researchers focus on relevant issues. Introducing Bayesian networks will show if the system can be of interest for a broader range of research questions and could ultimately lead to a fundamental change in research methodology.
On this page:
- Dr. M.J.N. Stols-Witlox (University of Amsterdam)
- Prof. dr. M.J. Sjerps (Netherlands Forensic Institute)
- Dr. E. Hendriks (Van Gogh Museum, University of Amsterdam)
- Prof. dr. L. van Tilborgh (Van Gogh Museum, University of Amsterdam)
- Prof.dr. A. Wallert (Rijksmuseum, University of Amsterdam)
- Netherlands Forensic Institute
- University of Amsterdam
- Van Gogh Museum
Want to know more?
Please click here for project updates and background information.