13 papers · ranked by Valyu relevance
Dilan Pathirana, Frank T. Bergmann, Domagoj Doresic, Polina Lakrisenko + 8 more
A central question in mathematical modeling of biological systems is determining which processes are relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the specification of…
Dilan Pathirana, Frank T. Bergmann, Domagoj Doresic, Polina Lakrisenko + 8 more
A central question in mathematical modeling of biological systems is determining which processes are most relevant and how they can be described. There are often competing hypotheses, which yield different models. Model comparison requires parameter optimization and sampling methods. Yet, standards for the…
Joram Soch, Carsten Allefeld
In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for statistical model inference. We introduce a new SPM toolbox for model…
Nicolas Lartillot
There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises…
Clive J. Hoggart
There is increasing interest in developing point of care tests to diagnose disease and predict prognosis based upon biomarker signatures of RNA or protein expression levels. Technology to measure the required biomarkers accurately and in a time-frame useful to health care professionals will be easier to develop by…
Georg Manthey, Miriam Liedvogel, Birgen Haest, Michael Manthey + 1 more
The ability to select statistical models based on how well they fit an empirical dataset is a central tenet of modern bioscience. How well this works, though, depends on how goodness-of-fit is measured. Likelihood and its derivatives (e.g. AIC) are popular and powerful tools when measuring goodness-of-fit, though…
David J. Warne, Ruth E. Baker, Matthew J. Simpson
Reaction–diffusion models describing the movement, reproduction and death of individuals within a population are key mathematical modelling tools with widespread applications in mathematical biology. A diverse range of such continuum models have been applied in various biological contexts by choosing different flux and…
Eugenio Piasini, Shuze Liu, Pratik Chaudhari, Vijay Balasubramanian + 1 more
Occam’s razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones^1^. This principle is thought to play a role in human perception and decision-making^2^, but the nature of our presumed preference for simplicity is not understood. Here we use preregistered…
Aaditya Prasad Gupta
Biological systems, at all scales of organization from nucleic acids to ecosystems, are inherently complex and variable. Therefore mathematical models have become an essential tool in systems biology, linking the behavior of a system to the interaction between its components. Parameters in empirical mathematical models…
Stefano Palminteri, Valentin Wyart, Etienne Koechlin
Cognitive neuroscience, especially in the fields of learning and decision-making, is witnessing the blossoming of computational model-based analyses. Several methodological and review papers have indicated how and why candidate models should be compared by trading off their ability to predict the data as a function of…
James R. H. Cooke, Luc P. J. Selen, Robert J. van Beers, W. Pieter Medendorp
Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Matthias Borgstede, Patrick Anselme
We present a new methodology to partition different sources of behavior change within a selectionist framework based on the Price equation – the Multilevel Model of Behavioral Selection (MLBS). The MLBS provides a theoretical background to describe behavior change in terms of operant selection. Operant selection is…