AIMC Topic: Probability

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Dynamical Mechanism of Sampling-Based Probabilistic Inference Under Probabilistic Population Codes.

Neural computation Feb 17, 2022
Animals make efficient probabilistic inferences based on uncertain and noisy information from the outside environment. It is known that probabilistic population codes, which have been proposed as a neural basis for encoding probability distributions,...
Neural Networks, Computer Animals Probability
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RoBoT: a robust Bayesian hypothesis testing method for basket trials.

Biostatistics (Oxford, England) Oct 13, 2021
A basket trial in oncology encompasses multiple "baskets" that simultaneously assess one treatment in multiple cancer types or subtypes. It is well-recognized that hierarchical modeling methods, which adaptively borrow strength across baskets, can im...
Robotics Bayes Theorem Research Design Computer Simulation Humans Probability Medical Oncology
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Measuring the importance of individual units in producing the collective behavior of a complex network.

Chaos (Woodbury, N.Y.) Sep 1, 2021
A quantitative evaluation of the contribution of individual units in producing the collective behavior of a complex network can allow us to understand the potential damage to the structure integrity due to the failure of local nodes. Given a time ser...
Neural Networks, Computer North America Probability
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Power Function Error Initialization Can Improve Convergence of Backpropagation Learning in Neural Networks for Classification.

Neural computation Jul 26, 2021
Supervised learning corresponds to minimizing a loss or cost function expressing the differences between model predictions yn and the target values tn given by the training data. In neural networks, this means backpropagating error signals through th...
Neural Networks, Computer Algorithms Probability
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Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Proceedings of the National Academy of Sciences of the United States of America Jul 13, 2021
Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasiona...
Female Reproducibility of Results Algorithms Phenotype Mice Mice, Inbred C57BL Flow Cytometry Animals Male Germ-Line Mutation Machine Learning Software Automation Meiosis Leukocytes Probability
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Using large-scale experiments and machine learning to discover theories of human decision-making.

Science (New York, N.Y.) Jun 11, 2021
Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this go...
Humans Models, Psychological Choice Behavior Decision Making Machine Learning Neural Networks, Computer Probability Deep Learning
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Machine Learning for Causal Inference: On the Use of Cross-fit Estimators.

Epidemiology (Cambridge, Mass.) May 1, 2021
BACKGROUND: Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly robust cross-fit estimators have been prop...
Humans Machine Learning Probability Models, Statistical Computer Simulation Bias Causality
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Better-than-chance classification for signal detection.

Biostatistics (Oxford, England) Apr 10, 2021
The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is pa...
Supervised Machine Learning Probability Neuroimaging Humans Computer Simulation
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Generative transfer learning for measuring plausibility of EHR diagnosis records.

Journal of the American Medical Informatics Association : JAMIA Mar 1, 2021
OBJECTIVE: Due to a complex set of processes involved with the recording of health information in the Electronic Health Records (EHRs), the truthfulness of EHR diagnosis records is questionable. We present a computational approach to estimate the pro...
Humans Machine Learning Diagnosis Professional-Patient Relations Delivery of Health Care Supervised Machine Learning Electronic Health Records Probability Disease
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Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis Jan 1, 2021
In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociod...
Female Algorithms Middle Aged Probability Humans Intensive Care Units Male Venous Thrombosis Machine Learning Cost-Benefit Analysis
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