eLife

High-frequency spike inference with particle Gibbs sampling

A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...
AHA/ASA Journals

Observational Comparative Research in Cardiovascular and Brain Health and Disease: A Scientific Statement From the American Heart Association

Resources for observational comparative research have expanded enormously in recent years to include very large sources of ...

What is Model Collapse in AI? Causes, Signs, Consequences and Prevention

Discover what AI model collapse is, its causes, early warning signs, and severe consequences. Learn how to prevent recursive ...
IEEE

Bayesian Local Sampling-Based Planning

Abstract: Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness.
jech.bmj

Estimating causal effects from epidemiological data

1 Department of Epidemiology, Harvard School of Public Health, Boston, USA 2 Departments of Epidemiology and Biostatistics, Harvard School of Public Health Correspondence to: Dr M A Hernán Department ...
CBS

Improving the sampling strategy for the Community Innovation Survey using machine learning algorithms

National statistical institutes (NSI's) are increasingly interested in using non-probability data to produce official statistics. Examples are information on the internet, social media messages, ...
Investopedia

Sampling Distribution: Definition, How It's Used, and Example

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Thomas J. Brock is a CFA and CPA with more ...
VentureBeat

Researchers find adding this one simple sentence to prompts makes AI models way more creative

One of the coolest things about generative AI models — both large language models (LLMs) and diffusion-based image generators — is that they are "non-deterministic." That is, despite their reputation ...
Monash University

Statistical inference

In statistics, a population refers to the entire group of individuals or items that we are interested in studying. However, collecting data from the whole population is often impractical due to size, ...
BMJ

Our Health Counts Toronto: using respondent-driven sampling to unmask census undercounts of an urban indigenous population in Toronto, Canada

Objectives To provide evidence of the magnitude of census undercounts of ‘hard-to-reach’ subpopulations and to improve estimation of the size of the urban indigenous population in Toronto, Canada, ...
Scientific Research Publishing

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S. et al. (2020). Purposive Sampling: Complex or Simple? Research Case Examples. Journal of Research in ...

ABSTRACT: The evolving landscape of organizational leadership necessitates exploring how transformational leadership can be adapted and embraced in contemporary settings. This research investigates ...
The Conversation

How a subfield of physics led to breakthroughs in AI – and from there to this year’s Nobel Prize

John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work ...