Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...
Artificial Intelligence (AI) is transforming industries by automating processes, improving decision-making, and enhancing ...
A new AI model called COMPASS uses tumor gene expression data to predict which patients will respond to cancer immunotherapy drugs called immune checkpoint inhibitors. Analyzing past data from 16 ...
Abstract: In this article, we introduce a dynamic generative model, the Bayesian allocation model (BAM), for modeling count data. BAM covers various probabilistic nonnegative tensor factorization (NTF ...
In light of recent global shocks and rising external volatility, there is a growing need to effectively monitor short-term economic fluctuations, especially in countries with limited access to ...
In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using a Gaussian graphical model combined with ...
ABSTRACT: Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Instead of evaluating the likelihood function, ABC ...
Abstract: In many parameter estimation problems, the exact model is unknown. In such cases, a predetermined data-based selection rule selects a parametric model from a set of candidates before the ...
Quantum designers: Florian Marquardt (left) and Leopoldo Sarra have shown how deep Bayesian experimental design can be applied to quantum many-body systems. (Courtesy: Leopoldo Sarra) As quantum ...
Kulprit requires a working Python interpreter (3.12+). Assuming a standard Python environment is installed on your machine (including pip), Kulprit itself can be ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results