25 papers · ranked by Valyu relevance
Dominic Coey, Kenneth E. Hung
We study the problem of selecting the best m units from a set of n as m / n → α ∈ (0, 1 ), where noisy, heteroskedastic measurements of the units' true values are available and the decision-maker wishes to maximize the average true value of the units selected. Given a parametric prior distribution, the empirical Bayes…
Simon L. Cotter
There are many issues that can cause problems when attempting to infer model parameters from data. Data and models are both imperfect, and as such there are multiple scenarios in which standard methods of inference will lead to misleading conclusions; corrupted data, models which are only representative of subsets of…
Sierra A. Bainter, Thomas G. McCauley, Mahmoud M. Fahmy, Zachary T. Goodman + 2 more
'Zachary T. Goodman' 'Lauren B. Kupis' 'J. Sunil Rao'] In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network…
Bart van Erp, Wouter W. L. Nuijten, Thijs van de Laar, Bert de Vries + 1 more
'Patrick Shafto'] Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper…
Mahdi Nouraie, Connor Smith, Samuel Müller
Stability selection is a versatile framework for structure estimation and variable selection in high-dimensional setting, primarily grounded in frequentist principles. In this paper, we propose an enhanced methodology that integrates Bayesian analysis to refine the inference of inclusion probabilities within the…
Jim E. Griffin
selection using Cartesian credible sets Authors: ['Jim E. Griffin'] Modern regression applications can involve hundreds or thousands of variables which motivates the use of variable selection methods. Bayesian variable selection defines a posterior distribution on the possible subsets of the variables (which are…
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…
Kaixin Yang, Long Liu, Yalu Wen
Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian…
Daniel García Rasines, G. A. Young
This paper explores the challenges of constructing suitable inferential models in scenarios where the parameter of interest is determined in light of the data, such as regression after variable selection. Two compelling arguments for conditioning converge in this context, whose interplay can introduce ambiguity in the…
Yuehao Xu, Andreas Futschik, Ritabrata Dutta
Given the often intractable exact likelihood, likelihood-free inference plays an important role in population genetics. Indeed, several methodological developments in approximate Bayesian Computation (ABC) were inspired by applications in population genetics. Here, we explore a novel combination of recently proposed…
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…
Stijn Hawinkel, Olivier Thas, Steven Maere
The winner’s curse is a form of selection bias that arises when estimates are obtained for a large number of features, but only a subset of most extreme estimates is reported. It occurs in large scale significance testing as well as in rank-based selection, and imperils reproducibility of findings and follow-up study…
Márton Csillag, Hamza Giaffar, Eörs Szathmáry, Mauro Santos + 1 more
Building on the algorithmic equivalence between finite population replicator dynamics and particle filtering based approximation of Bayesian inference, we design a computational model to demonstrate the emergence of Darwinian evolution over representational units when collectives of units are selected to infer…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Mohammadreza Shiri, Sajjad Moharramnejad, Afshar Estakhr, Sharareh Fareghi + 5 more
'Sharareh Fareghi' 'Hamid Najafinezhad' 'Saeed Khavari Khorasani' 'Aziz Afarinesh' 'Morteza Eshraghi-Nejad' 'Vignesh Muthusamy'] Plant breeders are increasingly utilizing stability parameters as valuable tools for selecting cultivars in the context of genotype × environment interaction (GEI). Neglecting GEI in…
Roi Naveiro, Becky Tang
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points…
Jonas Verhellen
In recent years, there have been considerable academic and industrial research efforts to develop novel generative models for high-performing, small molecules. Traditional, rules-based algorithms such as genetic algorithms [Jensen, Chem. Sci., 2019, 12, 3567-3572] have, however, been shown to rival deep learning…
Yuehao Xu, Sherman Khoo, Andreas Futschik, Ritabrata Dutta
In this manuscript, we present an innovative Bayesian framework tailored for the inference of the selection coefficients in multi-locus Wright-Fisher models. Utilizing a signature kernel score, our approach offers an innovative solution for approximating likelihoods by extracting informative signatures from the…
Sikta Das Adhikari, Yuehua Cui, Jianrong Wang
Genome-wide Association Studies (GWAS) methods have identified individual single-nucleotide polymorphisms (SNPs) significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic…
Julien Diot, Hiroyoshi Iwata
Introduction Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not…
Yifan Wu, Aron Walsh, Alex Ganose
What is the minimum number of experiments, or calculations, required to find an optimal solution? Relevant chemical problems range from identifying a compound with target functionality within a given phase space to controlling materials synthesis and device fabrication conditions. A common feature in this application…
Matthew Stephens
I discuss the benefits of looking through the ‘Bayesian lens’ (seeking a Bayesian interpretation of ostensibly non-Bayesian methods), and the dangers of wearing ‘Bayesian blinkers’ (eschewing non-Bayesian methods as a matter of philosophical principle). I hope that the ideas may be useful to scientists trying to…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Lucy Cui, Stephanie Lo, Zili Liu
Decisions are often made under uncertainty. The most that one can do is use prior knowledge (e.g., base rates, prior probabilities, etc.) and make the most probable choice given the information we have. Unfortunately, most people struggle with Bayesian reasoning. Poor performance within Bayesian reasoning problems has…