Results 21 to 30 of about 2,910,377 (176)

Statistical learning and memory [PDF]

Cognition, 2020
Learners often need to identify and remember recurring units in continuous sequences, but the underlying mechanisms are debated. A particularly prominent candidate mechanism relies on distributional statistics such as Transitional Probabilities (TPs).
Ansgar D. Endress, Lauren K. Slone, Scott P. Johnson   +2 more
openaire   +3 more sources

Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models [PDF]

Hydrology and Earth System Sciences
A linear regression model is developed to link anomalies of streamflow to anomalies of precipitation amounts and temperature with the goal of making multi-decadal streamflow projections based on CMIP6 multi-model simulations.
M. Scheuerer, E. Byermoen, E. Byermoen, J. Ribeiro de Oliveira, T. Roksvåg, D. Vikhamar Schuler   +5 more
doaj   +1 more source

Machine Learning for Neuroimaging with Scikit-Learn [PDF]

, 2013
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series.
Abraham, Alexandre, Eickenberg, Michael, Gervais, Philippe, Gramfort, Alexandre, Kossaifi, Jean, Muller, Andreas, Pedregosa, Fabian, Thirion, Bertrand, Varoquaux, Gäel   +8 more
core   +4 more sources

Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits

Agronomy
The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense
Maurício de Oliveira Celeri, Weverton Gomes da Costa, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Cosme Damião Cruz, Vitor Seiti Sagae, Moysés Nascimento   +6 more
doaj   +1 more source

Optional Defaultable Markets

Risks, 2017
The paper deals with defaultable markets, one of the main research areas of mathematical finance. It proposes a new approach to the theory of such markets using techniques from the calculus of optional stochastic processes on unusual probability spaces ...
Mohamed N. Abdelghani, Alexander V. Melnikov   +1 more
doaj   +1 more source

Statistical Learning Within Objects

Psychological Science, 2023
Research has recently shown that efficient selection relies on the implicit extraction of environmental regularities, known as statistical learning . Although this has been demonstrated for scenes, similar learning arguably also occurs for objects.
van Moorselaar, Dirk, Theeuwes, Jan
openaire   +4 more sources

Machine learning phases in statistical physics [PDF]

, 2018
Conventionally, the study of phases in statistical mechan- ics is performed with the help of random sampling tools. Among the most powerful are Monte Carlo simulations consisting of a stochastic importance sampling over state space and evaluation of ...
Chen, Qi, Ph. D.
core   +1 more source

Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms

Heliyon
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide.
Saad Sahriar, Sanjida Akther, Jannatul Mauya, Ruhul Amin, Md Shahajada Mia, Sabba Ruhi, Md Shamim Reza   +6 more
doaj   +1 more source

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Scientific Reports, 2023
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery ...
Austin Clyde, Xuefeng Liu, Thomas Brettin, Hyunseung Yoo, Alexander Partin, Yadu Babuji, Ben Blaiszik, Jamaludin Mohd-Yusof, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens   +12 more
doaj   +1 more source

Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm

Engineering Reports
Machine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of ...
Jannatul Mauya, Saad Sahriar, Sanjida Akther, Ruhul Amin, Sabba Ruhi, Md. Shamim Reza   +5 more
doaj   +1 more source