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相关概念视频

Modeling with Differential Equations01:25

Modeling with Differential Equations

4
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...
4
Econometric Views (EViews)01:29

Econometric Views (EViews)

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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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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

1.1K
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...
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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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相关实验视频

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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贝叶斯式FitForecast:一个用户友好的R工具箱,用于用普通微分方程进行参数估计和预测.

Hamed Karami1, Amanda Bleichrodt2, Ruiyan Luo2

  • 1Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA.

BMC medical informatics and decision making
|October 15, 2025
PubMed
概括

贝叶斯式FitForecast是一个新的R工具箱,简化了对普通微分方程 (ODE) 模型的贝叶斯式参数估计和预测. 它降低了复杂的动态系统的编码障碍,增强了公共卫生和流行病学决策.

关键词:
贝叶斯的校准是贝叶斯的校准.贝叶斯的推理 贝叶斯的推理流行病学建模的流行病学建模.预测 预测 预测 预测哈密尔顿的蒙特卡洛蒙特卡洛的时间.在MCMC采样中采集的样本.模型选择 模型选择ODE参数估计的估计方法斯坦 R 接口 斯坦 R 接口不确定性量化不确定性的量化.

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科学领域:

  • 计算生物学和生物信息学
  • 流行病学和公共卫生.
  • 数学建模的数学建模

背景情况:

  • 普通微分方程 (ODEs) 对于科学和医疗保健中的动态系统建模至关重要.
  • 对ODE模型的贝叶斯校准和预测通常需要广泛的编码专业知识.
  • 需要可访问的工具来促进动态系统中的贝叶斯推理.

研究的目的:

  • 介绍贝叶斯式FitForecast,一个用户友好的R工具箱.
  • 简化ODE模型的贝叶斯参数估计和预测.
  • 降低应用贝叶斯方法在健康信息学和公共卫生中的技术障碍.

主要方法:

  • 为ODE模型自动生成Stan文件.
  • 用户友好的界面用于模型配置和预先定义.
  • 应用到历史流行病数据集 (例如,1918年流感,1896年孟买瘟疫) 和模拟数据.
  • 评估参数估计和预测性能.

主要成果:

  • 证明了可靠的参数估计和预测.
  • 成功应用到现实世界和模拟的流行病数据.
  • 在不同的观测误差结构下验证的性能 (Poisson,负二项式).
  • 提供了全面的模型性能评估工具.

结论:

  • 提高时间序列建模和预测先进贝叶斯方法的可访问性.
  • 在医疗预测和流行病学研究中广泛应用.
  • 包括一个交互式的闪亮的网络应用程序和教程视频用于用户支持.