The 'Essence of Volatility' That Bitcoin Investors Misunderstand—The Market Learning Process Exposed by Statistical Thinking

Bitcoin investors say that 'volatility is high.' However, most of them misunderstand the true cause.Prices fluctuate ...
BIOENGINEER

Response Times Expose Hidden Brain Mechanisms Behind Learning and Mental Illness

By jointly modeling response times alongside choices, researchers disentangled working memory, cognitive control and ...
The University of Texas at Arlington

UTA uses AI to uncover drivers of eye disease

National Eye Institute grant supports research to identify new targets for preserving vision and treating eye disease ...

Quantifying Central Bank Credibility: Reading Market Reactions via Bayesian Updating

In March 2023, the moment the Swiss National Bank (SNB) announced a rapid interest rate hike, the financial markets were ...
BIOENGINEER

Machine Learning Leads Soil Mapping Race, but No Single Model Wins Everywhere

A first-of-its-kind systematic review of 190 studies finds that HASM, Euclidean-enhanced machine learning, and Bayesian ...
quantumzeitgeist

Researchers Define Limit for Telling Quantum States Apart

Distinguishing between multiple quantum states becomes dramatically easier as the number of measurements increases ...
Crypto Briefing

Multi-agent AI framework breaches government systems, steals thousands of records in four-day operation

A multi-agent AI system breached government infrastructure in Asia over four days, stealing 2,564 records in the first confirmed attack of its ...
Medical Xpress

Benefits of using a Bayesian statistical framework for replicating research

The "replication crisis" refers to a problem in the sciences where findings from previous experiments don't hold up when studies are repeated. It is a particular issue for those in the behavioral ...
Nature

Non-Bayesian Learning in Social Networks

Non-Bayesian learning in social networks refers to a paradigm in which distributed agents iteratively update probabilistic beliefs about an underlying state by combining private observations with the ...
marktechpost

Predicting and Interpreting In-Context Learning Curves Through Bayesian Scaling Laws

Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities, where they can learn tasks from demonstrations without requiring additional training. A critical ...