ArticleProceedings of the National Academy of Sciences of the United States of America2018
Maximizing the information learned from finite data selects a simple model.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 56 citations in OpenAlex.
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- Individual yeast cells signal at different levels but each with good precision.Royal Society open science · 2025Article
- Far from Asymptopia: Unbiased High-Dimensional Inference Cannot Assume Unlimited Data.Entropy (Basel, Switzerland) · 2023Article
- Sloppiness: Fundamental study, new formalism and its application in model assessment.PloS one · 2023Article
- Information geometry for multiparameter models: new perspectives on the origin of simplicity.Reports on progress in physics. Physical Society (Great Britain) · 2022Review
- Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics.Science (New York, N.Y.) · 2022Article
- Causal Geometry.Entropy (Basel, Switzerland) · 2020Article
- How Could Future AI Help Tackle Global Complex Problems?Frontiers in robotics and AI · 2020Article
- Practical Use of Regularization in Individualizing a Mathematical Model of Cardiovascular Hemodynamics Using Scarce Data.Frontiers in physiology · 2020Article
- Learning dynamical information from static protein and sequencing data.Nature communications · 2019Article
- A high-bias, low-variance introduction to Machine Learning for physicists.Physics reports · 2019Article
- Parameter estimation and identifiability in a neural population model for electro-cortical activity.PLoS computational biology · 2019Article
- Cellular packing, mechanical stress and the evolution of multicellularity.Nature physics · 2018Article
- Optimizing information transmission in optogenetic Wnt signaling.Physical review researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
We use the language of uninformative Bayesian prior choice to study the selection of appropriately simple effective models. We advocate for the prior which maximizes the mutual information between parameters and predictions, learning as much as possible from limited data. When many parameters are poorly constrained by the available data, we find that this prior puts weight only on boundaries of the parameter space. Thus, it selects a lower-dimensional effective theory in a principled way, ignoring irrelevant parameter directions. In the limit where there are sufficient data to tightly constrain any number of parameters, this reduces to the Jeffreys prior. However, we argue that this limit is pathological when applied to the hyperribbon parameter manifolds generic in science, because it leads to dramatic dependence on effects invisible to experiment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.