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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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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...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Multi-input and Multi-variable systems01:22

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Hidden Markov models for extended batch data.

Laura L E Cowen1, Panagiotis Besbeas2,3, Byron J T Morgan3

  • 1Mathematics and Statistics, University of Victoria, PO Box 1700 STN CSC, Victoria BC, Canada, V8W 2Y2.

Biometrics
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Summary
This summary is machine-generated.

This study introduces new methods for analyzing batch-marked wild animal populations, incorporating unmarked individuals. These hidden Markov models efficiently estimate survival and population size, improving wildlife research.

Keywords:
Batch markingIntegrated population modelingMark-recaptureOpen N-mixture modelsViterbi algorithmWeather-loach

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Area of Science:

  • Ecology
  • Wildlife Population Dynamics
  • Statistical Modeling

Background:

  • Batch marking is crucial for estimating wild animal survival and population size, especially for elusive species.
  • Existing methods often ignore unmarked individuals in samples, leading to potential biases.
  • There's a need for unified models that incorporate all individuals for more accurate ecological assessments.

Purpose of the Study:

  • To develop novel likelihood methods for extended batch-marking experiments, including unmarked individuals.
  • To unify models for marked and unmarked individuals using hidden Markov models (HMMs).
  • To enable simultaneous estimation of population size, survival, and immigration.

Main Methods:

  • Developed likelihood functions for extended batch-marking experiments incorporating unmarked individuals.
  • Utilized hidden Markov models (HMMs) to unify the analysis of marked and unmarked animals.
  • Employed integrated population modeling for combined parameter estimation and model evaluation.

Main Results:

  • Demonstrated that models for marked and unmarked individuals are HMMs, providing a unified framework.
  • Showcased efficient computation and maximization of likelihoods for complex ecological models.
  • Validated HMM methods through simulations, confirming excellent performance and efficiency, even with many hidden states.

Conclusions:

  • The developed HMM approach offers a unified and efficient method for analyzing batch-marked populations, including unmarked individuals.
  • This framework facilitates simultaneous estimation of key demographic parameters like population size, survival, and immigration.
  • The methods provide a significant advancement over previous complex procedures, enhancing wildlife population estimation accuracy.