Empirical investigation requires dealing with fundamental uncertainty. In experimental psychology, research questions are often addressed using Null Hypothesis Significance Testing (NHST), an approach ...

A production-grade Python package for modeling financial time series using Bayesian jump-diffusion processes. This package implements 9 advanced models, comprehensive risk metrics, portfolio ...

Abstract: The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for ...

According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...

Abstract: This paper presents a comprehensive Bayesian inference framework for analyzing and quantifying industrial load rebound effects through systematic decomposition. The proposed model decomposes ...

Probabilistic reasoning is central to many theories of human cognition, yet its foundations are often presented through abstract mathematical formalisms disconnected from the logic of belief and ...

Cybersecurity researchers have uncovered critical remote code execution vulnerabilities impacting major artificial intelligence (AI) inference engines, including those from Meta, Nvidia, Microsoft, ...