The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...
Abstract: Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network ...
Technologies that underpin modern society, such as smartphones and automobiles, rely on a diverse range of functional ...
We use Bayesian meta-analysis methods to estimate the impact of unconditional cash transfers (UCTs). Aggregating evidence from 115 studies of 72 UCT programs in middle and low income countries, we ...
Incrementality testing in Google Ads is suddenly within reach for far more advertisers than before. Google has lowered the barriers to running these tests, making lift measurement possible even ...
This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...
Abstract: Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of ...
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