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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

166
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
166
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

152
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...
152
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

147
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...
147
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

117
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...
117
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

217
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
217
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

845
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...
845

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Related Experiment Video

Updated: Nov 1, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Validating model-based Bayesian integration using prior-cost metamers.

Hansem Sohn1, Mehrdad Jazayeri2,3

  • 1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America
|June 23, 2021
PubMed
Summary
This summary is machine-generated.

Humans may not always use Bayesian decision theory for choices under uncertainty. This study introduces a new method to test model-based Bayesian computations, finding they apply to timing but not motor rotation tasks.

Keywords:
Bayesian integrationinternal modelsensorimotor learning

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Area of Science:

  • Cognitive Neuroscience
  • Decision Science
  • Computational Psychology

Background:

  • Human decision-making under uncertainty is explained by two main theories: Bayesian decision theory and trial-and-error learning.
  • Bayesian decision theory suggests optimal behavior arises from internal models of experience (priors) and outcomes (cost functions).
  • An alternative view proposes optimization via trial and error without explicit internal models.

Purpose of the Study:

  • To differentiate between model-based Bayesian computations and simpler learning strategies in human decision-making.
  • To introduce a novel experimental paradigm to probe the use of internal models (priors and cost functions) during decision-making.
  • To test the generalizability of model-based Bayesian computations across different cognitive tasks.

Main Methods:

  • Developed a paradigm focusing on 'metamers'—situations where different internal models yield the same optimal decision policy.
  • Tested human participants in two experiments: an interval timing task and a visuomotor rotation task.
  • Analyzed behavioral data to infer the underlying decision-making strategy, specifically looking for evidence of explicit Bayesian computations.

Main Results:

  • The proposed paradigm successfully distinguished between different internal models in decision-making.
  • Bayesian learning strategies were validated in the interval timing task.
  • Evidence for explicit model-based Bayesian computations was not found in the visuomotor rotation task.

Conclusions:

  • Human decision-making under uncertainty does not exclusively rely on explicit model-based Bayesian computations.
  • The findings suggest that the application of Bayesian learning strategies is task-dependent.
  • The developed paradigm offers a domain-general approach to investigate the extent of model-based Bayesian computations in humans.