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

Histogram01:05

Histogram

15.9K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
15.9K
Probability Histograms01:17

Probability Histograms

12.9K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
12.9K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

976
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
976
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

346
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
346
Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

104
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
104
Two-dimensional Gel Electrophoresis01:22

Two-dimensional Gel Electrophoresis

7.0K
Two-dimensional gel electrophoresis is a high-resolution protein separation method first introduced by O' Farrell and Klose in 1975. This method involves protein separation by two dimensions, mass and charge, making it more accurate than one-dimensional gel electrophoresis.
The first dimension separation uses the isoelectric focusing or IEF technique performed on immobilized pH gradient (IPG) strips that separate proteins according to their isoelectric points.
Biological samples, such...
7.0K

You might also read

Related Articles

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

Sort by
Same author

Soft fundamental measure theory functional for the Weeks-Chandler-Andersen repulsive potential.

Physical review. E·2023
Same author

Statistical Mechanical Model of Gas Adsorption in a Metal-Organic Framework Harboring a Rotaxane Molecular Shuttle.

Langmuir : the ACS journal of surfaces and colloids·2020
Same author

Stochastic approximation Monte Carlo with a dynamic update factor.

Physical review. E·2020
Same author

Theoretical Prediction of Disrupted Min Oscillation in Flattened Escherichia coli.

PloS one·2015
Same author

Approach to approximating the pair distribution function of inhomogeneous hard-sphere fluids.

Physical review. E, Statistical, nonlinear, and soft matter physics·2014
Same author

Improved association in a classical density functional theory for water.

The Journal of chemical physics·2014

Related Experiment Video

Updated: Dec 5, 2025

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

8.8K

Flat-histogram method comparison on the two-dimensional Ising model.

Jordan K Pommerenck1, David Roundy1

  • 1Department of Physics, Oregon State University, Corvallis, Oregon 97331, USA.

Physical Review. E
|October 20, 2020
PubMed
Summary
This summary is machine-generated.

The stochastic approximation with a dynamic update factor (SAD) method robustly estimates the density of states for the Ising model. Unlike pure Wang-Landau (WL), SAD and WL with a production run offer reliable convergence without prior energy range knowledge.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
10:27

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

12.8K

Related Experiment Videos

Last Updated: Dec 5, 2025

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

8.8K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K
Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
10:27

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

12.8K

Area of Science:

  • Computational Physics
  • Statistical Mechanics

Background:

  • Flat-histogram methods are crucial for estimating the density of states in complex systems.
  • The Wang-Landau (WL) method is a widely used technique, but can suffer from convergence issues.

Purpose of the Study:

  • To compare the convergence and performance of various flat-histogram methods for the two-dimensional Ising model.
  • To evaluate the efficacy of the novel stochastic approximation with a dynamic update factor (SAD) method.

Main Methods:

  • Comparison of stochastic approximation Monte Carlo (SAMC), Wang-Landau (WL), 1/t-WL, and SAD methods.
  • Implementation of a WL method followed by a production run for entropy refinement.

Main Results:

  • WL followed by a production run converges to the true density of states, unlike pure WL.
  • SAD, 1/t-WL, and WL with a production run demonstrate robust convergence.
  • SAD method does not require prior knowledge of the energy range.

Conclusions:

  • WL with a production run outperforms other WL variants, ensuring ergodicity and detailed balance.
  • SAD offers a robust and efficient alternative for density of states estimation, particularly when the energy range is unknown.