25 papers · ranked by Valyu relevance
Karl Friston, Will Penny
This note describes a Bayesian model selection or optimization procedure for post hoc inferences about reduced versions of a full model. The scheme provides the evidence (marginal likelihood) for any reduced model as a function of the posterior density over the parameters of the full model. It rests upon specifying…
Daniel G. Rasines, G. A. Young
We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesian approach under selection. Second, we propose two types of noninformative priors for selection models. These priors may be employed to…
Snigdha Panigrahi, Jonathan Taylor
Modeled along the truncated approach in Panigrahi et al. (2016), selection-adjusted inference in a Bayesian regime is based on a selective posterior. Such a posterior is determined together by a generative model imposed on data and the selection event that enforces a truncation on the assumed law. The effective…
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…
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…
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…
James R. H. Cooke, Luc P. J. Selen, Robert J. van Beers, W. Pieter Medendorp
Comparing models facilitates testing different hypotheses regarding the computational basis of perception and action. Effective model comparison requires stimuli for which models make different predictions. Typically, experiments use a predetermined set of stimuli or sample stimuli randomly. Both methods have…
Yu Lin Hsu, Chu Chuan Jeng, Pavithra Sripathanallur Murali, Mohammadreza Torkjazi + 3 more
'Mohammadreza Torkjazi' 'J. S. West' 'Michaela Zuber' 'Vadim Sokolov'] This paper presents an overview of some of the concepts of Bayesian Learning. The number of scientific and industrial applications of Bayesian learning has been growing in size rapidly over the last few decades (Damien et al. 2013). This process has…
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…
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…
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…
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…
Flavia Alves da Silva, Alexandre Pio Viana, Caio Cezar Guedes Correa, Eileen Azevedo Santos + 4 more
'Eileen Azevedo Santos' 'Julie Anne Vieira Salgado de Oliveira' 'José Daniel Gomes Andrade' 'Rodrigo Moreira Ribeiro' 'Leonardo Siqueira Glória'] Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making.…
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…
Takeshi Hayashi, Hiroyoshi Iwata
Background In genomic selection, a model for prediction of genome-wide breeding value (GBV) is constructed by estimating a large number of SNP effects that are included in a model. Two Bayesian methods based on MCMC algorithm, Bayesian shrinkage regression (BSR) method and stochastic search variable selection (SSVS)…
John O. Campbell
Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian…
Aryan Deshwal, Cory Simon, Janardhan Rao Doppa
Given a gas storage or separation task, we wish to search a library of nanoporous materials (NPMs) for the one with the optimal adsorption property. The high cost of measuring the adsorption property of an NPM, whether in the lab or a simulation, precludes exhaustive search. We explain, demonstrate, and advocate…
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…
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…
John O. Campbell
Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian…
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…
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…
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…