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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

60
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...
60
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

366
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...
366
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.0K
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...
4.0K
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

399
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...
399
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K

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相关实验视频

Updated: Jun 9, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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结构稀疏的贝叶斯神经网络的尖端和平行收缩价格.

Sanket Jantre, Shrijita Bhattacharya, Tapabrata Maiti

    IEEE transactions on neural networks and learning systems
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    此摘要是机器生成的。

    本研究介绍了结构稀疏的贝叶斯神经网络 (BNNs),使用拉索和马先验进行高效的深度学习模型压缩. 提出的方法有效地削减节点,提高预测准确性和减少延迟.

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    科学领域:

    • 深度学习和人工智能
    • 计算神经科学和机器学习
    • 贝叶斯推理和统计建模的贝叶斯推理.

    背景情况:

    • 深度学习模型经常因过度参数化而遭受高复杂性和计算低效.
    • 稀少的深度学习通过减少网络大小以提高性能和效率提供解决方案.
    • 模型压缩技术对于在资源有限的环境中部署深度神经网络至关重要.

    研究的目的:

    • 探索拉索和马收缩技术来压缩贝叶斯神经网络 (BNNs).
    • 提出和开发结构稀疏的BNN,使用新的尖和石的先验.
    • 为拟议的模型建立理论保证,并证明它们的实证有效性.

    主要方法:

    • 实施尖和板块集团拉索 (SS-GL) 和尖和板块集团马 (SS-GHS) 结构化稀疏性的先验.
    • 开发可计算可处理的变量推理方法,包括连续放松伯努利变量.
    • 基于网络拓和权重的变化后部收缩速率的理论分析.

    主要成果:

    • 拟议的结构稀疏的BNN实现了与基线模型相比具有竞争力的预测准确性.
    • 通过系统地修剪过多的网络节点来实现显著的模型压缩.
    • 经验证明,推断延迟减少和计算效率提高.

    结论:

    • 具有SS-GL和SS-GHS priors的结构稀疏的BNN为深度学习模型压缩提供了有效的方法.
    • 开发的变量推理方法为训练这些稀疏模型提供了可计算的解决方案.
    • 这些发现突出了BNN结构化的稀疏性在实现高效和准确的深度学习系统方面的潜力.