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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

319
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
319
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

1.4K
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...
1.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

387
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...
387
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

309
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
309
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

568
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,...
568
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

644
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
644

You might also read

Related Articles

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

Sort by
Same author

Communication in the face of death and dying - how does the encounter with death influence the patient management competence of medical students? An outcome-evaluation.

BMC medical education·2022
Same author

First results of a national external quality assessment scheme for the detection of SARS-CoV-2 genome sequences.

Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology·2020
Same author

Reduced seroprevalence against vaccine preventable diseases (VPDs) in adult patients with cancer: necessity of routine vaccination as part of the therapeutic concept.

Annals of oncology : official journal of the European Society for Medical Oncology·2020
Same author

Letter: retreatment of patients with chronic hepatitis C who have failed interferon-free combination therapy with direct acting anti-virals.

Alimentary pharmacology & therapeutics·2016
Same author

Emergence of tick-borne encephalitis in new endemic areas in Austria: 42 years of surveillance.

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin·2015
Same author

Durability of SVR in chronic hepatitis C patients treated with peginterferon-α2a/ribavirin in combination with a direct-acting anti-viral.

Alimentary pharmacology & therapeutics·2013
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Nonparametric identification and maximum likelihood estimation for hidden Markov models.

G Alexandrovich1, H Holzmann1, A Leister1

  • 1Department of Mathematics and Computer Science, Philipps-Universität Marburg, Hans-Meerweinstraße, 35032 Marburg, Germany , alexandrovich@mathematik.uni-marburg.de   leister@mathematik.uni-marburg.de.

Biometrika
|June 10, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces nonparametric methods for hidden Markov models, enabling accurate parameter and order identification. These techniques ensure reliable estimation even with complex state-dependent distributions.

Keywords:
Hidden Markov modelLatent state modelNonparametric identificationNonparametric maximum likelihood estimation

More Related Videos

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

794
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

3.8K

Related Experiment Videos

Last Updated: Mar 19, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

794
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

3.8K

Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Hidden Markov models (HMMs) are widely used for sequential data analysis.
  • Accurate parameter estimation and model order identification are crucial for HMMs.
  • Existing methods often rely on strong parametric assumptions.

Purpose of the Study:

  • To develop nonparametric identification and maximum likelihood estimation methods for finite-state HMMs.
  • To establish theoretical guarantees for these nonparametric methods.
  • To investigate the numerical performance of the proposed estimators.

Main Methods:

  • Nonparametric identification of HMM parameters and order under full-rank, ergodic transition matrices and distinct state-dependent distributions.
  • Development of nonparametric maximum likelihood estimation theory.
  • Utilizing Kullback-Leibler divergence for nonparametric identification.
  • Establishing consistency of the nonparametric maximum likelihood estimator for mixture distributions.

Main Results:

  • Identification of HMM parameters and order is achieved nonparametrically.
  • The Kullback-Leibler divergence nonparametrically identifies the true parameter vector.
  • The nonparametric maximum likelihood estimator is consistent for mixture distributions without assuming identification of mixing distributions.
  • Simulation studies assess numerical properties and goodness-of-fit tests.

Conclusions:

  • Nonparametric approaches provide robust identification and estimation for HMMs.
  • The developed methods relax restrictive distributional assumptions.
  • These findings advance the theoretical and practical application of HMMs in various fields.