Zobrazeno 1 - 10
of 145
pro vyhledávání: '"Pensar, Johan"'
Confounding bias and selection bias are two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition. We introduce the Sequential Adjust
Externí odkaz:
http://arxiv.org/abs/2401.16990
Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncer
Externí odkaz:
http://arxiv.org/abs/2312.06531
Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding
Externí odkaz:
http://arxiv.org/abs/2205.11234
Autor:
Pavlović, Milena, Hajj, Ghadi S. Al, Kanduri, Chakravarthi, Pensar, Johan, Wood, Mollie, Sollid, Ludvig M., Greiff, Victor, Sandve, Geir Kjetil
Machine learning is increasingly used to discover diagnostic and prognostic biomarkers from high-dimensional molecular data. However, a variety of factors related to experimental design may affect the ability to learn generalizable and clinically app
Externí odkaz:
http://arxiv.org/abs/2204.09291
Autor:
Pascoe, Ben, Futcher, Georgina, Pensar, Johan, Bayliss, Sion C., Mourkas, Evangelos, Calland, Jessica K., Hitchings, Matthew D., Joseph, Lavin A., Lane, Charlotte G., Greenlee, Tiffany, Arning, Nicolas, Wilson, Daniel J., Jolley, Keith A., Corander, Jukka, Maiden, Martin C.J., Parker, Craig T., Cooper, Kerry K., Rose, Erica B., Hiett, Kelli, Bruce, Beau B., Sheppard, Samuel K.
Publikováno v:
In Journal of Infection November 2024 89(5)
Publikováno v:
In Decision Support Systems March 2024 178
Publikováno v:
Scandinavian Journal of Statistics, Vol. 44: 455-479, 2017
Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressive dependence structures, several generalizations of Markov networks h
Externí odkaz:
http://arxiv.org/abs/2103.15540
Publikováno v:
Stat Comput 31, 73 (2021)
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically assume a fully connected model which provides no direct insight to the mo
Externí odkaz:
http://arxiv.org/abs/2011.01484
We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables
Externí odkaz:
http://arxiv.org/abs/2010.00684
Markov networks are widely studied and used throughout multivariate statistics and computer science. In particular, the problem of learning the structure of Markov networks from data without invoking chordality assumptions in order to retain expressi
Externí odkaz:
http://arxiv.org/abs/1910.13832