Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An AI-powered Bayesian Generative Modeling Approach for Causal Inference in Observational Studies.

Journal of the American Statistical Association·2026

Same author

Cross-modality representation and multi-sample integration of spatially resolved omics data.

Briefings in bioinformatics·2026

Same author

A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation.

Nature communications·2026

Same author

Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis.

Nature communications·2026

Same author

Author Correction: Sensitive detection of tumor mutations from blood and its application to immunotherapy prognosis.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Nov 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Density estimation using deep generative neural networks.

Qiao Liu^1,2,3,4, Jiaze Xu^2,3,4,5,6, Rui Jiang⁷

¹Ministry of Education Key Laboratory of Bioinformatics, Research Department of Bioinformatics at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China.

Proceedings of the National Academy of Sciences of the United States of America

|April 9, 2021

Summary

This summary is machine-generated.

We introduce Roundtrip, a deep learning framework for density estimation. This method enhances generative adversarial networks (GANs) to provide accurate density value estimates, improving both data generation and estimation tasks.

Keywords:

GAN deep learning density estimation importance sampling neural network

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Nov 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Statistics
Machine Learning
Deep Learning
Computational Neuroscience

Background:

Density estimation is a core challenge in statistics and machine learning.
Deep generative models like GANs excel at data generation but often lack explicit density estimation capabilities.
Existing neural density estimators impose strict constraints on data transformation.

Purpose of the Study:

To propose Roundtrip, a novel computational framework for general-purpose density estimation.
To integrate generative capabilities with accurate density value estimation using deep neural networks.
To overcome limitations of previous neural density estimators by enabling more general data transformations.

Main Methods:

Developed Roundtrip, a framework leveraging deep generative neural networks.
Utilized manifold learning to model target densities from base distributions (e.g., Gaussian).
Established a statistical framework for Generative Adversarial Networks (GANs) enabling explicit density evaluation.

Main Results:

Roundtrip successfully combines generative power with density estimation.
The framework supports general mappings from latent to data space.
Numerical experiments demonstrated state-of-the-art performance across various density estimation tasks.

Conclusions:

Roundtrip offers a powerful and flexible approach to density estimation.
The framework advances the capabilities of deep generative models.
Roundtrip provides a statistically sound method for density estimation with GANs.