Correcting prevalence estimation for biased sampling with testing errors von Zhou, Lili, Díaz‐Pachón, Daniel Andrés, Zhao, Chen, Rao, J. Sunil, Hössjer, Ola

Volltext

Mode hunting through active information von Díaz‐Pachón, Daniel Andrés, Sáenz, Juan Pablo, Rao, J. Sunil, Dazard, Jean‐Eudes

Volltext

Sometimes Size Does Not Matter von Díaz-Pachón, Daniel Andrés, Hössjer, Ola, Marks, Robert J.

Volltext

A Note on Large Deviations for the Stable Marriage of Poisson and Lebesgue with Random Appetites von Díaz Pachón, Daniel Andrés

Volltext

Assessing, testing and estimating the amount of fine-tuning by means of active information von Daniel Andrés Díaz-Pachón, Hössjer, Ola

Volltext

Percolation for the stable marriage of Poisson and Lebesgue with random appetites von Daniel Andrés Díaz-Pachón

Volltext

Active information requirements for fixation on the Wright-Fisher model of population genetics von Daniel Andrés Díaz-Pachón, Marks, Robert J

Volltext

Generalized active information: extensions to unbounded domains von Daniel Andrés Díaz-Pachón, Marks, Robert J

Volltext

Is Cosmological Tuning Fine or Coarse? von Daniel Andrés Díaz-Pachón, Hössjer, Ola, Marks, Robert J

Volltext

A simple correction for COVID-19 sampling bias von Daniel Andrés Díaz-Pachón, J Sunil Rao

Volltext

Hypothesis testing with active information von Daniel Andrés Díaz-Pachón, Juan Pablo Sáenz, J Sunil Rao

Volltext

Mode hunting through active information von Díaz-Pachón, Daniel Andrés, Sáenz, Juan Pablo, Rao, J. Sunil, Dazard, Jean-Eudes

Volltext

Assessing, Testing and Estimating the Amount of Fine-Tuning by Means of Active Information von Díaz-Pachón, Daniel Andrés, Hössjer, Ola

Volltext

Is It Possible to Know Cosmological Fine-tuning? von Díaz-Pachón, Daniel Andrés, Hössjer, Ola, Mathew, Calvin

Volltext

An Information Theoretic Approach to Prevalence Estimation and Missing Data von Hossjer, Ola, Diaz-Pachon, Daniel Andres, Zhao, Chen, Rao, J. Sunil

Volltext

"Back to the future" projections for COVID-19 surges von Rao, J Sunil, Liu, Tianhao, Díaz-Pachón, Daniel Andrés

Volltext

High Dimensional Mode Hunting Using Pettiest Components Analysis von Liu, Tianhao, Diaz-Pachon, Daniel Andres, Rao, J. Sunil, Dazard, Jean-Eudes

Volltext

Is cosmological tuning fine or coarse? von Díaz-Pachón, Daniel Andrés, Hössjer, Ola, Marks II, Robert J.

Volltext

Hypothesis testing with active information von Díaz–Pachón, Daniel Andrés, Sáenz, Juan Pablo, Rao, J. Sunil

Volltext

A Formal Framework for Knowledge Acquisition: Going beyond Machine Learning von Hössjer, Ola, Díaz-Pachón, Daniel Andrés, Rao, J Sunil

Volltext