Results 71 to 80 of about 149,758 (276)
Advanced Science, EarlyView.Oligonucleotide‐based fluorescence in situ hybridization probes were developed in the model citrus species Citrus maxima. These probes were applied to comparative karyotyping across 14 species in the Rutaceae family. This analysis revealed chromosomal evolution in lineages that diverged from Citrus nearly 52 million years ago.
Li He +9 more
wiley +1 more source
Hierarchical models of pain: Inference, information-seeking, and adaptive control.
NeuroImage, 2020 Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour.
Ben Seymour, Flavia Mancini
doaj +1 more source
Measure Transformer Semantics for Bayesian Machine Learning [PDF]
Logical Methods in Computer Science, 2013 The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables.
Johannes Borgström +4 more
doaj +1 more source
Correcting Predictions for Approximate Bayesian Inference
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corrects for inaccuracies in posterior
Klami Arto +2 more
openaire +4 more sources
Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy
Advanced Science, EarlyView.Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu +4 more
wiley +1 more source
An effective likelihood-free approximate computing method with statistical inferential guarantees [PDF]
, 2018 Approximate Bayesian computing is a powerful likelihood-free method that has grown increasingly popular since early applications in population genetics.
Li, Wentao +2 more
core +1 more source
Approximate Bayesian Inference Based on Expected Evaluation
Bayesian AnalysiszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hammer, Hugo L. +2 more
openaire +2 more sources
Advanced Science, EarlyView.ABSTRACT The muscle capsule of Trichinella is a critical structure that impedes immune attacks and drug penetration, yet the molecular mechanisms underlying its formation remain poorly understood. Using a high‐quality super‐pangenome comprising 12 Trichinella species, we compared extensive genomic variations between encapsulating and non‐encapsulating ...
Qingbo Lv +8 more
wiley +1 more source
Reparameterization invariance in approximate Bayesian inference
Advances in Neural Information Processing Systems 37Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign different posterior densities to different parametrizations of identical functions.
Roy, Hrittik +6 more
+6 more sources
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
, 2018 Bayesian inference typically requires the computation of an approximation to the posterior distribution. An important requirement for an approximate Bayesian inference algorithm is to output high-accuracy posterior mean and uncertainty estimates ...
Broderick, Tamara +3 more
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