Affiner les résultats

Probabilistic models with deep neural networks

Masegosa, Andrés R. ; Cabañas, Rafael ; Langseth, Helge ; Nielsen, Thomas D. ; Salmerón, Antonio

In: Entropy, 2021, vol. 23, no. 1, p. 27 p

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical...

Inferring gene targets of drugs and chemical compounds from gene expression profiles

Noh, Heeju ; Gunawan, Rudiyanto

In: Bioinformatics, 2016, vol. 32, no. 14, p. 2120-2127

Using Bayesian hierarchical parameter estimation to assess the generalizability of cognitive models of choice

Scheibehenne, Benjamin ; Pachur, Thorsten

In: Psychonomic Bulletin & Review, 2015, vol. 22, no. 2, p. 391-407

A Bayesian approach for comparing cross-validated algorithms on multiple data sets

Corani, Giorgio ; Benavoli, Alessio

In: Machine Learning, 2015, vol. 100, no. 2-3, p. 285-304

Approximate maximum likelihood estimation for population genetic inference

Bertl, Johanna ; Ewing, Gregory ; Kosiol, Carolin ; Futschik, Andreas

In: Statistical Applications in Genetics and Molecular Biology, 2017, vol. 16, no. 5-6, p. 291-312

Robust Bayesian model averaging for the analysis of presence-absence data

Corani, Giorgio ; Mignatti, Andrea

In: Environmental and Ecological Statistics, 2015, vol. 22, no. 3, p. 513-534

Deviant Processing in the Primary Somatosensory Cortex

Musall, Simon ; Haiss, Florent ; Weber, Bruno ; von der Behrens, Wolfger

In: Cerebral Cortex, 2017, vol. 27, no. 1, p. 863-876

Orthologous Matrix (OMA) algorithm 2.0: more robust to asymmetric evolutionary rates and more scalable hierarchical orthologous group inference

Train, Clément-Marie ; Glover, Natasha M. ; Gonnet, Gaston H. ; Altenhoff, Adrian M. ; Dessimoz, Christophe

In: Bioinformatics, 2017, vol. 33, no. 14, p. i75-i82

Asymptotic analysis of estimators on multi-label data

Streich, Andreas ; Buhmann, Joachim

In: Machine Learning, 2015, vol. 99, no. 3, p. 373-409

The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

Bodory, Hugo ; Camponovo, Lorenzo ; Huber, Martin ; Lechner, Michael

In: Journal of Business and Economic Statistics, 2020, vol. 38, no. 1, p. 183-200

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German...