Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Self-Discrepancy Theory02:45

Self-Discrepancy Theory

18.9K
One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
18.9K
Self-Discrepancy and Its Effects01:29

Self-Discrepancy and Its Effects

306
Self-discrepancy theory explains how people compare their actual self to their ideal and ought selves and how mismatches between these self-guides can lead to emotional distress. Developed by E. Tory Higgins, the theory distinguishes among three components of self-concept: the actual self, the ideal self, and the ought self. These refer respectively to how individuals perceive themselves, how they aspire to be, and how they believe they are obligated to be. Emotional well-being, self-esteem,...
306
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.6K
What are Estimates?01:06

What are Estimates?

8.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.2K
Density00:56

Density

19.3K
Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...
19.3K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

215
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
215

You might also read

Related Articles

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

Sort by
Same author

Convergence rates of a partition based Bayesian multivariate density estimation method.

Advances in neural information processing systems·2019
Same author

Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks.

PLoS computational biology·2017
Same author

Safety and Survival Benefit of Surgical Management for Elderly Gastric Cancer Patients.

Hepato-gastroenterology·2015
Same author

Evaporative cooling over the Tibetan Plateau induced by vegetation growth.

Proceedings of the National Academy of Sciences of the United States of America·2015
Same author

Helicobacter pylori infection is associated with type 2 diabetes among a middle- and old-age Chinese population.

Diabetes/metabolism research and reviews·2015
Same author

The Value of Palliative Gastrectomy for Gastric Cancer Patients With Intraoperatively Proven Peritoneal Seeding.

Medicine·2015
Same journal

QuanDA: Quantile-Based Discriminant Analysis for High-Dimensional Imbalanced Classification.

Advances in neural information processing systems·2026
Same journal

Analysis of Variance of Multiple Causal Networks.

Advances in neural information processing systems·2026
Same journal

Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning.

Advances in neural information processing systems·2026
Same journal

Distributionally Robust Feature Selection.

Advances in neural information processing systems·2026
Same journal

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same journal

Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.

Advances in neural information processing systems·2026
See all related articles

Related Experiment Video

Updated: Jan 21, 2026

Author Spotlight: An Adapted Optical Density-Based Microplate Assay for Characterizing Actinobacteriophage Infection
03:33

Author Spotlight: An Adapted Optical Density-Based Microplate Assay for Characterizing Actinobacteriophage Infection

Published on: June 30, 2023

2.8K

Density Estimation via Discrepancy Based Adaptive Sequential Partition.

Dangna Li1, Kun Yang2, Wing Hung Wong3

  • 1ICME, Stanford University, Stanford, CA 94305.

Advances in Neural Information Processing Systems
|July 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonparametric method for density estimation, approximating probability density functions with piecewise constant functions. The approach uses discrepancy to control partitioning, offering an efficient and provable method for data analysis and k-means initialization.

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

932
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

Related Experiment Videos

Last Updated: Jan 21, 2026

Author Spotlight: An Adapted Optical Density-Based Microplate Assay for Characterizing Actinobacteriophage Infection
03:33

Author Spotlight: An Adapted Optical Density-Based Microplate Assay for Characterizing Actinobacteriophage Infection

Published on: June 30, 2023

2.8K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

932
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Accurate probability density estimation is crucial for understanding data distributions.
  • Existing methods may lack efficiency or provable convergence rates for nonparametric density estimation.
  • Nonparametric approaches offer flexibility in modeling complex, unknown distributions.

Purpose of the Study:

  • To develop a novel nonparametric method for approximating probability density functions (PDFs).
  • To create a piecewise constant density estimate on a binary partition of the domain.
  • To leverage discrepancy measures for efficient and controlled partitioning.

Main Methods:

  • Utilized independent and identically distributed (i.i.d.) observations from an unknown continuous distribution.
  • Employed a binary partitioning strategy for the domain Ω.
  • Integrated discrepancy measures from Quasi Monte Carlo analysis to guide the partitioning process.

Main Results:

  • Developed a simple and efficient nonparametric density estimation algorithm.
  • Achieved a piecewise constant function as the density estimate.
  • Demonstrated a provable convergence rate for the proposed method.
  • Empirically validated the efficiency of the density estimation technique.

Conclusions:

  • The proposed method provides an effective nonparametric approach to density estimation.
  • The use of discrepancy offers a robust mechanism for partitioning the domain.
  • The algorithm's efficiency and provable convergence make it suitable for practical applications.
  • The method shows potential for improving k-means clustering initializations.