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...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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

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...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

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

Sort by
Same author

A High-Fidelity RNA-Targeting Cas13X Downregulates Connexin43 in Macroglia: A Novel Neuroprotective Strategy for Glaucoma.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Toward a formal theory for computing machines made out of whatever physics offers.

Nature communications·2023
Same author

Role of Ambient Hydrogen in HiPIMS-ITO Film during Annealing Process in a Large Temperature Range.

Nanomaterials (Basel, Switzerland)·2022
Same author

Influence of Agaricus bisporus establishment and fungicidal treatments on casing soil metataxonomy during mushroom cultivation.

BMC genomics·2022
Same author

Oxygen sensing with individual ZnO:Sb micro-wires: effects of temperature and light exposure on the sensitivity and stability.

Royal Society open science·2022
Same author

Complete Genome Sequence of the Plant-Pathogenic Fungus <i>Colletotrichum lupini</i>.

Molecular plant-microbe interactions : MPMI·2021
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Videos

A bound on modeling error in observable operator models and an associated learning algorithm.

Ming-Jie Zhao1, Herbert Jaeger, Michael Thon

  • 1Department of Computer Science, Jacobs University, Bremen 28759, Germany. mingjie.zhao@gmail.com

Neural Computation
|June 25, 2009
PubMed
Summary
This summary is machine-generated.

A new error-controlling algorithm for observable operator models (OOMs) ensures assured error bounds in learned models. This approach optimizes auxiliary matrices for improved statistical efficiency in machine learning tasks.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Observable operator models (OOMs) generalize hidden Markov models (HMMs) with a matrix formalism.
  • Existing OOM learning algorithms are fast but depend on optimizing auxiliary transformation matrices.
  • Optimizing these matrices is challenging, lacking accessible computational criteria.

Purpose of the Study:

  • Derive a bound on OOM modeling error in terms of auxiliary matrices.
  • Develop an optimization procedure for these matrices.
  • Introduce a complete, error-controlling OOM learning algorithm.

Main Methods:

  • Expressing OOM modeling error bounds using auxiliary matrices.
  • Developing an optimization procedure for auxiliary matrices based on error bounds.
  • Implementing the error-controlling algorithm for OOM learning.

Main Results:

  • A novel method to bound OOM modeling error is established.
  • An optimization procedure for auxiliary matrices is derived.
  • The error-controlling algorithm provides assured error bounds for learned OOM parameters.

Conclusions:

  • The error-controlling algorithm offers a complete learning solution for OOMs.
  • This method enhances the statistical efficiency and reliability of OOMs.
  • Performance is validated against HMMs and other OOM learning algorithms.