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相关概念视频

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

503
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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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...
250
State Space Representation01:27

State Space Representation

534
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
534
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

243
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...
243
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

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According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
582
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
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超越时间同质性对于连续时间多态马尔科夫模型.

Emmett B Kendall1, Jonathan P Williams1,2, Gudmund H Hermansen2,3,4

  • 1Department of Statistics, North Carolina State University.

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|June 2, 2025
PubMed
概括

如果假定时间均,连续时间马尔科夫模型可能会有偏差. 本研究提出了一种时间不均模型的方法,提高了医疗记录等复杂数据的参数估计准确度.

关键词:
阿伦 - 约翰森估计器隐藏的马尔科夫模型层次化的贝叶斯模型模型.纵向研究是一项纵向研究.国家空间模型

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科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 计算生物学 计算生物学

背景情况:

  • 多态马尔科夫模型是随机过程的标准.
  • 连续时间马尔科夫过程模型不规则地观察到的数据,在纵向研究中很常见.
  • 时间均模型有分析解决方案,但时间不均模型没有.

研究的目的:

  • 为了说明参数估计中的偏差,当假定时间均性时.
  • 在真正的时间不均马尔科夫模型中提出一种概率计算方法.
  • 解决多状态马尔科夫模型中的状态标签错误分类问题.

主要方法:

  • 从科尔莫戈罗夫前期方程中推导概率函数.
  • 使用矩阵指数式解决方案用于时间均的过程.
  • 倡导贝叶斯计算以避免MLEs的数值梯度近似.

主要成果:

  • 证明潜在的参数估计偏差,当断片均假设被违反时.
  • 开发一个时间不均概率计算的框架.
  • 应用到具有状态错误分类的多状态马尔科夫模型.

结论:

  • 断片时间同质性的假设可以导致显著的偏差.
  • 对于精确建模复杂的随机过程,需要一种时间不均的方法.
  • 贝叶斯方法为这些模型中的参数估计提供了一个有效的替代方案.