In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...

StatsPAI is the agent-native Python package for causal inference and applied econometrics. One import, 800+ functions, covering the complete empirical research workflow — from classical econometrics ...

Bayesian inference offers a principled framework for estimating the key parameters of queueing models by combining prior knowledge with observed data. In service and manufacturing environments, ...

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

Google says its new TurboQuant method could improve how efficiently AI models run by compressing the key-value cache used in LLM inference and supporting more efficient vector search. In tests on ...

Industry groups and drugmakers want the US Food and Drug Administration (FDA) to explicitly clarify that Bayesian statistical methods can be used for products beyond those intended for children and ...

Ultimately, the stock best positioned to win is likely ASML (ASML 2.31%). Admittedly, that may come as a surprise, particularly because the company bills itself as the "most important company you've ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Birgitta Böckeler, Distinguished Engineer at ...

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