GitHub

krishshekhar/bayesian-switching-lds

Multivariate observations All numeric CSV columns (except Date) are stacked into (y_t \in \mathbb{R}^D) for model fitting. Sticky HDP (weak limit) Discrete modes (z_t \in {0,\ldots,L-1}) with global ...
Investopedia

Calculate Sales Tax: Simple Steps and Real-Life Examples

Elysse Bell is a finance and business writer for Investopedia. She writes about small business, personal finance, technology, and more. Erika Rasure is globally-recognized as a leading consumer ...
a-z-animals

Crows and monkeys show mental shortcuts when judging numbers

For centuries, humans have assumed that our thinking errors set us apart from the rest of the animal kingdom. From misjudging risks to seeing patterns that aren’t there, we like to imagine that our ...
EurekAlert!

AI-powered oil spill prediction system improves accuracy by up to 25%, with significant potential to enhance emergency response.

Breakthrough technology combines traditional physics-based modelling with artificial intelligence to improve oil spill trajectory predictions significantly. Significant accuracy gains achieve up to 20 ...
C&EN

Enhancement of Li-Ion Battery Cathode Performance via Bayesian and Genetic Algorithm Optimization: Experimental and Numerical Analysis

Department of Intelligent Energy and Industry, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea Department of Intelligent Energy and Industry, Chung-Ang University, 84 ...
Nature

Parameter Estimation In Differential Equation Models

Parameter estimation in differential equation models is a critical endeavour in the mathematical modelling of dynamic systems. Such models, represented by ordinary differential equations (ODEs), ...
marktechpost

GraphIC: A Novel Machine Learning Approach that Leverages Graph-based Representations of Reasoning Processes Coupled with Bayesian Networks (BNs) to Select In-Context Examples ...

In-context learning (ICL) enables LLMs to adapt to new tasks by including a few examples directly in the input without updating their parameters. However, selecting appropriate in-context examples ...
Frontiers

An introduction to Bayesian simulation-based inference for quantum machine learning with examples

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of ...
C&EN

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan ERATO Maeda Artificial Intelligence for Chemical Reaction Design and Discovery ...
Oak Ridge National Lab

Bayesian merging of numerical modeling and remote sensing for saltwater intrusion quantification in the Vietnamese Mekong Delta

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of ...
MIT Technology Review

Google DeepMind’s weather AI can forecast extreme weather faster and more accurately

It said Hurricane Lee would make landfall in Nova Scotia three days sooner than traditional methods predicted. This year the Earth has been hit by a record number of unpredictable extreme weather ...