Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

AI reasoning does not necessarily require spending huge amounts on frontier models. Instead, smaller models can yield ...
Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
eLife

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

This paper presents a valuable software package, named "Virtual Brain Inference" (VBI), that enables faster and more efficient inference of parameters in dynamical system models of whole-brain ...
The Motley Fool

What Is a Statistical Model? Overview and Example

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...
Scientific Research Publishing

Holland, P.W. (1986) Statistics and Causal Inference. Journal of the American Statistical Association, 81, 945-960.

ABSTRACT: Special education services are designed to provide tailored support for students with diverse learning needs, with the expectation of improving academic achievement. This study examines the ...
The Robot Report

Boston Dynamics and TRI use large behavior models to train Atlas humanoid

To be useful, humanoid robots will need to be competent at many tasks, according to Boston Dynamics. They must be able to manipulate a diverse range of objects, from small, delicate objects to large, ...
GitHub

YOLOv8-CPP-Inference example running an incorrect result using GPU

I compiled and ran Example YOLOv8-CPP-Inference in my own environment, and found that everything worked fine when using CPU for inference, but when using GPU for inference, the results became strange.
Association for Psychological Science

Powerful Myths: Common Misconceptions About Statistical Power

In the wake of the replication crisis, statistical power has become one of the central issues in debates about the quality of research. The widespread use of tests with low power is seen as a key ...
PNAS

Correction for Porwal et al., Comparing methods for statistical inference with model uncertainty

The authors note that the full replication code is now available at https://github.com/Anupreet-Porwal/Porwal-Raftery-PNAS-2022. The online version has been updated ...
IEEE

Synthetic Aperture Radar Deep Statistical Imaging Through Diffusion Generative Model Conditional Inference

Abstract: Synthetic aperture radar (SAR) plays a crucial role in remote sensing because of its ability to operate in all weather conditions, both day and night. The traditional FFT-based SAR imaging ...
Scientific Research Publishing

Wasserman, L. (2004) All of Statistics: A Concise Course in Statistical Inference. Springer Science.

ABSTRACT: This study investigates the application of the two-parameter Weibull distribution in modeling state holding times within HIV/AIDS progression dynamics. By comparing the performance of the ...