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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

97
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...
97
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

98
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...
98
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

582
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...
582
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

184
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,...
184
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

139
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...
139
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K

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相关实验视频

Updated: Jul 24, 2025

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

11.4K

使用循环神经网络进行边际贝叶斯后推理,并应用于顺序模型.

Thayer Fisher1, Alex Luedtke1, Marco Carone1

  • 1University of Washington, Department of Biostatistics.

Statistica Sinica
|July 6, 2023
PubMed
概括

这项研究引入了一种新的深度学习方法,使用循环神经网络 (RNN) 来近似贝叶斯数据分析中的后置量数. 这种方法避免了复杂的抽样方法和概率计算,为多维问题提供了更有效的替代方案.

科学领域:

  • 统计 统计 统计 统计
  • 机器学习 机器学习
  • 计算科学 计算科学

背景情况:

  • 在贝叶斯分析中,评估后方量数至关重要,尤其是在形成后方间隔时.
  • 像马尔科夫链蒙特卡罗 (MCMC) 和近似贝叶斯计算 (ABC) 这样的传统方法与多维问题和非结合先验作斗争.
  • 这些方法通常需要计算密集的采样或分析近似.

研究的目的:

  • 在贝叶斯数据分析中开发一种新,高效的方法来近似后置量.
  • 将量子值评估重新定义为一个可以通过深度神经网络解决的多任务学习问题.
  • 在复杂的多维场景中克服现有方法的局限性.

主要方法:

  • 利用反复深度神经网络 (RNN) 来近似后方量子.
  • 把这个问题作为一个多任务学习挑战.
  • 开发了一种风险最小化方法,可以绕过后续采样或概率计算的需要.

主要成果:

  • 证明了RNN在对贝叶斯推理的后方量值进行近似的有效性.
  • 展示了该方法在时间序列数据中的适用性,这是由于RNN的顺序信息处理.
  • 通过几个说明性例子验证了方法.
关键词:
贝叶斯深度学习是贝叶斯的深度学习.机器学习是机器学习.量化估计的量化估计.

相关实验视频

Last Updated: Jul 24, 2025

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

11.4K

结论:

  • 循环深度神经网络在贝叶斯分析中为近似后方量子提供了一个强大而有效的替代方案.
  • 拟议的方法简化了复杂的贝叶斯计算,避免了直接后端采样和概率评估.
  • 这种深度学习方法显示出显著的前景,特别是在时间序列和高维贝叶斯问题上.