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

Introduction to R01:11

Introduction to R

422
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
422
Introduction to MATLAB01:24

Introduction to MATLAB

155
MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing,...
155
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
Bernoulli's Equation: Problem Solving01:16

Bernoulli's Equation: Problem Solving

1.4K
A Venturi meter is essential for measuring fluid flow rates in pipelines. It utilizes the relationship between fluid velocity and pressure described by Bernoulli's equation. When installed in a sewage system, the Venturi meter accurately determines the wastewater flow rate by measuring pressure differences.
The first step is to compute the cross-sectional areas of the pipe and the Venturi throat to analyze the pressure difference indicated by the pressure gauge. Next, the continuity...
1.4K
Interpreting R Charts01:22

Interpreting R Charts

86
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
86
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81

You might also read

Related Articles

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

Sort by
Same author

The impact of obesity on inflammatory cytokines and 90- and 180-day survival in patients with alcohol-associated hepatitis.

Clinical and translational gastroenterology·2026
Same author

Cyclooxygenase-dependent contribution to increased flow mediated dilatation following a week of repeated ischemic preconditioning.

European journal of applied physiology·2026
Same author

Mean Arterial Pressure at Admission Predicts 28-day Mortality in Patients with Severe Alcohol-associated Hepatitis.

Clinical and translational gastroenterology·2026
Same author

Variable Selection in Multistate Models for Correlated Data With Application in a COVID-19 Vaccination Study.

Statistics in medicine·2026
Same author

SIRPα Cleavage Is Associated with Inflammatory Response, Corticosteroid Nonresponse, and Worse Prognosis in Clinical and Preclinical Models of Alcohol-Associated Hepatitis.

The American journal of pathology·2026
Same author

Assessment of early return to drinking in surviving patients with alcohol-associated hepatitis.

Alcohol, clinical & experimental research·2026

Related Experiment Video

Updated: Jul 23, 2025

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

643

Learning Hamiltonian Monte Carlo in R.

Samuel Thomas1, Wanzhu Tu1

  • 1Indiana University School of Medicine.

The American Statistician
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

Hamiltonian Monte Carlo (HMC) enhances Bayesian computation efficiency over traditional methods. This article explains HMC for statisticians and introduces an R package, hmclearn, for broader application.

Keywords:
Bayesian computationHamiltonian Monte CarloMCMCStan

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay
10:30

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Published on: November 5, 2017

8.9K

Related Experiment Videos

Last Updated: Jul 23, 2025

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria
08:33

Author Spotlight: An Optimized Automated Method for Investigating Retinoic Acid Receptors in Neuronal Mitochondria

Published on: July 28, 2023

643
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay
10:30

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay

Published on: November 5, 2017

8.9K

Area of Science:

  • Computational Statistics
  • Bayesian Inference
  • Statistical Computing

Background:

  • Hamiltonian Monte Carlo (HMC) is a computationally efficient algorithm for Bayesian inference.
  • Its origins in classical mechanics can be a barrier for statisticians.
  • Existing implementations may lack transparency for beginners.

Purpose of the Study:

  • To demystify Hamiltonian Monte Carlo (HMC) for a statistical audience.
  • To present an R implementation of HMC for broader accessibility.
  • To facilitate the application of HMC in diverse statistical models.

Main Methods:

  • Review of fundamental Hamiltonian Monte Carlo (HMC) concepts.
  • Development of an R package, hmclearn, for HMC implementation.
  • Demonstration of HMC using common statistical models in R.

Main Results:

  • An accessible explanation of HMC tailored for statisticians.
  • The introduction of hmclearn, an R package for HMC data analysis.
  • Illustrative examples showcasing HMC application in statistical modeling.

Conclusions:

  • Hamiltonian Monte Carlo (HMC) can be effectively applied by statisticians with appropriate resources.
  • The hmclearn R package lowers the barrier to entry for HMC implementation.
  • Promoting HMC adoption can advance Bayesian computation in statistical practice.