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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Probability Laws01:49

Probability Laws

Overview
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

You might also read

Related Articles

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

Sort by
Same author

Impact of Antiviral Therapy Scale-Up Among People Who Inject Drugs in Scotland: Regional Evidence of Hepatitis C Virus Elimination.

Liver international : official journal of the International Association for the Study of the Liver·2026
Same author

An asymptomatic <i>WASF1</i> truncation reveals pathogenic mechanism and therapeutic strategy for neurodevelopmental disorders.

Frontiers in behavioral neuroscience·2026
Same author

Development and validation of a nomogram for predicting recurrence in epithelial ovarian cancer after primary cytoreductive surgery: A single-center retrospective study.

Pakistan journal of medical sciences·2026
Same author

Primary cilia protect against intervertebral disc degeneration and spine scoliosis by regulating Hedgehog-P53-mediated cell apoptosis signaling.

Journal of advanced research·2026
Same author

Prognostic Value of Scoring Systems in Elderly Sepsis-Associated AKI Patients: A Multicenter Retrospective Cohort Study.

Journal of intensive care medicine·2026
Same author

Predicting 28-day and 90-day mortality in microbiologically-confirmed sepsis: a retrospective cohort study.

BMC infectious diseases·2026
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: May 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Maximum likelihood.

Shuying Yang1, Daniela De Angelis

  • 1GlaxoSmithKline Services Unlimited, Brentford, Middlesex, UK. shuying.y.yang@gsk.com

Methods in Molecular Biology (Clifton, N.J.)
|October 23, 2012
PubMed
Summary
This summary is machine-generated.

The maximum likelihood method provides a robust statistical approach for estimating unknown population parameters. This guide details its core concepts, theory, and practical applications for accurate data analysis.

More Related Videos

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Related Experiment Videos

Last Updated: May 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

A Tactile Automated Passive-Finger Stimulator (TAPS)
19:44

A Tactile Automated Passive-Finger Stimulator (TAPS)

Published on: June 3, 2009

Area of Science:

  • Statistics
  • Statistical Inference

Background:

  • Maximum likelihood estimation (MLE) is a fundamental statistical technique.
  • Widely applied across diverse scientific disciplines for parameter estimation.

Purpose of the Study:

  • To provide a comprehensive overview of the maximum likelihood method.
  • To explain its underlying concepts, theory, and properties of estimates.
  • To illustrate practical applications and implementation details.

Main Methods:

  • Description of core concepts and definitions in maximum likelihood estimation.
  • Explanation of the basic theory and properties of maximum likelihood estimates.
  • Introduction to confidence intervals and likelihood ratio tests.

Main Results:

  • Derivation of maximum likelihood estimates through practical examples.
  • Understanding the behavior and characteristics of MLEs.
  • Demonstration of hypothesis testing using likelihood ratio tests.

Conclusions:

  • Maximum likelihood estimation is a powerful and versatile tool for statistical inference.
  • The chapter equips readers with the knowledge to apply MLE in various contexts.
  • Resources for further learning and software implementation are provided.