Results 71 to 80 of about 2,810,332 (372)
Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings [PDF]
, 2011 Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
A Quinn +49 more
core +5 more sources
Shared Genetic Effects and Antagonistic Pleiotropy Between Multiple Sclerosis and Common Cancers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Epidemiologic studies have reported inconsistent altered cancer risk in individuals with multiple sclerosis (MS). Factors such as immune dysregulation, comorbidities, and disease‐modifying therapies may contribute to this variability.
Asli Buyukkurt +5 more
wiley +1 more source
Bayesian Estimation Under Informative Sampling
, 2016 Bayesian analysis is increasingly popular for use in social science and other application areas where the data are observations from an informative sample.
Savitsky, Terrance D., Toth, Daniell
core +1 more source
Clustering Algorithm Reveals Dopamine‐Motor Mismatch in Cognitively Preserved Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To explore the relationship between dopaminergic denervation and motor impairment in two de novo Parkinson's disease (PD) cohorts. Methods n = 249 PD patients from Parkinson's Progression Markers Initiative (PPMI) and n = 84 from an external clinical cohort.
Rachele Malito +14 more
wiley +1 more source
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
Front. Neuroinform., 2013 The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times.
Thomas V. Wiecki, Imri Sofer, M. Frank
semanticscholar +1 more source
Arthritis Care &Research, EarlyView.Objective In complex diseases, it is challenging to assess a patient's disease state, trajectory, treatment exposures, and risk of multiple outcomes simultaneously, efficiently, and at the point of care. Methods We developed an interactive patient‐level data visualization and analysis tool (VAT) that automates illustration of the trajectory of a ...
Ji Soo Kim +18 more
wiley +1 more source
Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems
Journal of Electrical and Computer Engineering, 2010 A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented.
Ashraf A. Tahat, Nikolaos P. Galatsanos
doaj +1 more source
, 2012 The problem of clock offset estimation in a two-way timing exchange regime is considered when the likelihood function of the observation time stamps is exponentially distributed.
Ahmad, Aitzaz +3 more
core +1 more source
Arthritis Care &Research, EarlyView.Objective This study examined the global and regional temporal changes in cross‐country inequalities of site‐specific osteoarthritis (OA) burden from 1990 to 2021. Methods Age‐standardized years lived with disability rates (ASYRs) for site‐specific OA across 204 countries and territories were obtained from the Global Burden of Diseases, Injuries, and ...
Haowei Chen +14 more
wiley +1 more source
The Bayesian Expectation-Maximization-Maximization for the 3PLM
Frontiers in Psychology, 2019 The current study proposes an alternative feasible Bayesian algorithm for the three-parameter logistic model (3PLM) from a mixture-modeling perspective, namely, the Bayesian Expectation-Maximization-Maximization (Bayesian EMM, or BEMM).
Shaoyang Guo +2 more
doaj +1 more source