Trajectory-oriented Bayesian experiment design versus Fisher A-optimal design: an in depth comparison study

Trajectory-oriented Bayesian experiment design versus Fisher A-optimal design: an in depth comparison study

MOTIVATION Experiment design strategies for biomedical models with the purpose of parameter estimation or model discrimination are in the focus of intense research. Experimental limitations such as sparse and noisy data result in unidentifiable parameters and render-related design tasks challenging problems. Often, the temporal resolution of data is a limiting factor and the amount of possible ...

An Efficient Bayesian Optimal Design for Logistic Model

Consider a Bayesian optimal design with many support points which poses the problem of collecting data with a few number of observations at each design point. Under such a scenario the asymptotic property of using Fisher information matrix for approximating the covariance matrix of posterior ML estimators might be doubtful. We suggest to use Bhattcharyya matrix in deriving the information matri...

Design-oriented Research versus Research-oriented Design

In this position paper, the two terms design-oriented research and research-oriented design are studied and discussed in some detail in relation to HCI. In designoriented research—where research is the area and design the means—the production of new knowledge and the questioning of problem descriptions and other kinds of limitations to the freedom of design are seen as characterizing elements. ...

A Bayesian approach to targeted experiment design

MOTIVATION Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. Howeve...

Statistical Experiment and Optimal Design