All articles tagged with #bayesian inference
The study demonstrates that mice encode and utilize prior information across the entire brain during decision-making, with neural signals reflecting Bayesian priors that influence behavior, and these signals are distributed across cortical and subcortical regions, involving complex bidirectional information flow.
Tamara Broderick, a newly tenured associate professor at MIT, uses Bayesian inference to quantify uncertainty and measure the robustness of data analysis techniques. Her focus is on understanding the limitations of statistical tools and collaborating with various fields to develop better tools for specific situations. Broderick's research includes projects such as developing a machine-learning model for more accurate predictions about ocean currents and creating a tool to help severely motor-impaired individuals use a computer's graphical user interface. Her passion for problem-solving extends to her hobby of collecting hiking patches with her husband, combining her love for the outdoors and exploration.
An international team of physicists led by Aleksi Vuorinen at the University of Helsinki has used Bayesian inference to conclude that there is an 80–90% likelihood of ultra-dense deconfined quark matter existing in the cores of the most massive neutron stars. This exotic state of matter, which is believed to have dominated the universe in the early moments after the Big Bang, could raise intriguing questions about astrophysical phenomena and the internal structure of neutron stars.
Scientists have discovered that the human brain is naturally wired to perform advanced calculations using Bayesian inference, a statistical method that combines prior knowledge with new evidence. This capability allows the brain to interpret the environment with precision and speed, unlike machines. Researchers have developed a mathematical model that closely mirrors the brain's visual data interpretation process, opening doors for advancements in fields like artificial intelligence and clinical neurology. Understanding the brain's inherent design for Bayesian inference can revolutionize machine learning and offer new strategies for therapeutic interventions in the future.
Researchers have discovered that the human brain naturally performs advanced calculations similar to high-powered computers through a process called Bayesian inference. This statistical method allows the brain to combine prior knowledge with new evidence, enabling precise and swift interpretation of the environment. The study reveals that the brain's visual system is inherently structured to execute Bayesian computations, offering insights into fields such as artificial intelligence and clinical neurology. The findings confirm existing theories and open doors for harnessing the brain's natural ability for practical applications that benefit society.
A study published in Nature Communications reveals that human brains are naturally wired to perform advanced calculations through Bayesian inference, a statistical method that combines prior knowledge with new evidence. The study's mathematical model closely matches how the brain processes visual information, confirming existing theories and opening doors for practical applications in fields such as artificial intelligence and clinical neurology. The research provides insights into the brain's inherent design for advanced processing and interpretation of sensory data.