24 papers · ranked by Valyu relevance
Mudd, Richard, Friedberg, Rina + 6 more
A "Winner's Curse" arises in large-scale online experimentation platforms [[1]] when the same experiments are used to both select treatments and evaluate their effects. In these settings, classical difference-in-means estimators of treatment effects are upwardly biased and conventional confidence intervals are rendered…
Kun Fan, Xiaoxi Li, Shejuty Devnath, Brock Olson + 2 more
Robust variable selection methods have emerged as powerful tools for dissecting high-dimensional gene-environment interactions in longitudinal studies, owing to their ability to accommodate intra-cluster correlations, capture structured sparsity, and handle heavy-tailed repeated measures. Despite these advantages…
Siddhartha R Dalal, Vishal Misra, Abhay Parekh
Prior work has shown that transformers can perform exact Bayesian filtering within a fixed hypothesis class. Can they also perform Bayesian model selection -- identifying the correct hypothesis class from data? We introduce model-selection Bayesian wind tunnels: controlled environments where ground-truth posteriors…
Adam J. Iqbal, Emmanuel O. Ogundimu, F. Javier Rubio
Sample selection models are a widely used approach for correcting bias caused by data that are missing not at random. Their formulation requires specifying the variables that influence the outcome and those that drive the selection process. This specification is often based on expert knowledge, which can result in the…
Jordan S. Martin, Yimen G. Araya‐Ajoy, Niels J. Dingemanse, Alastair J. Wilson + 1 more
Individual reaction norms describe how labile phenotypes vary as a function of organisms' expected trait values (intercepts) and plasticity across environments (slopes), as well as their degree of stochastic phenotypic variability or predictability (residuals). These reaction norms can be estimated empirically using…
Julia Reuter, Fabricio Olivetti de Franca
Symbolic regression (SR) is a class of methods that systematically explore the space of mathematical functions to discover models that accurately capture the underlying relationships in a dataset. Despite recent advances in the field, a lack of support for uncertainty quantification (UQ) limits its adoption in…
Carl J. Stone, Megan G. Behringer
Temporally structured environments are ubiquitous in nature, but time-dependent fitness effects are difficult to measure and thus understudied. To resolve temporal fitness structure at genome scale, we developed a Bayesian multilevel framework for longitudinal randomly barcoded transposon sequencing (RB-TnSeq) that…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
Hema Sri Sai Kollipara, Tapabrata Maiti, Sanjukta Chakraborty, Samiran Sinha
Genomics and other studies encounter many features and a selection of essential features with high accuracy is desired. In recent years, there has been a significant advancement in the use of Bayesian inference for variable (or feature) selection. However, there needs to be more practical information regarding their…
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
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Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
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Thorough treatment of conformation in computational chemistry is required to capture the subtle energy differences that lead to experimental observations. Accurate quantum chemistry calculations are very expensive and evaluation of the entire ensemble found during a conformational search is often unachievable. This is…
Hannah Fenwick, Guillermo Campitelli, Matthew B. Thompson
Research on Bayesian reasoning has been shaped by two productive traditions: ecological rationality, which explains why natural frequencies facilitate inference, and nested sets accounts, which show how transparent set relations support analytic reasoning. Together, these approaches have generated a rich empirical…
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Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
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The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Wesley C. DeMontigny, Charles F. Delwiche
Selective pressures can vary across both sites and evolutionary lineages; however, most codon models accommodate heterogeneity along only one of these dimensions and require the number of selective regimes to be specified in advance. Here, we introduce OmegaSwitch, a Bayesian phylogenetic software framework for…
Anirban Chakraborty, Chloe Mattila, Debashis Ghosh, Brian Neelon + 1 more
High-throughput bulk and single-cell omics technologies enable comprehensive molecular profiling, yet identifying compact, biologically interpretable marker sets that distinguish cell types, conditions, or disease states remains challenging. Standard pipelines rely on univariate differential expression tests, which…
Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman
Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a recently introduced multi-objective evolutionary algorithm for learning discretized BNs, enabling experts to trade-off different objectives of interest, such as…
Alexandros Ntagiantas, Panagiotis Tsilimidos, George Giannakopoulos, Christoforos Rekatsinas + 1 more
Advanced materials discovery increasingly relies on machine learning and Bayesian optimization to explore large discrete design spaces under limited evaluation budgets. However, conventional Bayesian optimization (BO) can become inefficient as candidate spaces grow, often evaluating low-value regions before reaching…
Soham Bakshi, Lingjun Gao, Zijun Gao, Snigdha Panigrahi
Researchers often select top-performing options or winners, based on a data-driven criterion, such as treatments, models, or model features and then report effect estimates for the selected winners. Naive post-selection estimates, however, are known to suffer from the winner's curse, producing systematically…
Authors not listed
Occupational chemical hazards pose profound risks to chemists in laboratory and industrial settings, encompassing acute and chronic exposures that imperil sensory organs (e.g., ocular, auditory, olfactory, dermal) and vital physiological systems. This manuscript delineates a multifaceted, innovative protocol suite…
Keming Zhang, Yaoyao Li, Jungang Zou, Sijian Wang + 2 more
Selecting important individual- and cluster-level predictors has become increasingly critical in healthcare research, where data often exhibit hierarchical structures due to collection from multiple clusters. Mixed-effects models, which account for within-cluster correlation and between-cluster heterogeneity, are a…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Stefan Dendorfer, Andreas M. Kist
Neural Architecture Search (NAS)-combined with biology-inspired evolutionary methods-can help discover suitable architectures tailored to a given objective. A guided evolutionary approach can enhance efficiency, aiming to accelerate the discovery of top-performing architectures within a given search space. We propose a…