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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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Multicompartment Models: Overview01:14

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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.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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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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Variability: Analysis01:11

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Vesicular Tubular Clusters01:45

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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Clustering Hidden Markov Models With Variational Bayesian Hierarchical EM.

Hui Lan, Ziquan Liu, Janet H Hsiao

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    This study introduces a new algorithm for clustering hidden Markov models (HMMs). It automatically determines the number of clusters and hidden states, improving performance on time-series data analysis.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Statistical Modeling

    Background:

    • Hidden Markov Models (HMMs) are widely used for time-series data.
    • Clustering HMMs is of growing interest in machine learning.
    • Determining the number of clusters (K) and hidden states (S) remains challenging.

    Purpose of the Study:

    • Propose a novel variational Bayesian hierarchical EM algorithm for HMM clustering.
    • Simultaneously learn cluster centers and automatically determine K and S.
    • Improve clustering performance and model complexity selection for HMMs.

    Main Methods:

    • Utilizes a variational Bayesian hierarchical EM algorithm.
    • Clusters HMMs based on their densities and priors.
    • Employs a prior on (K,S) and posterior probability approximation for selection.
    • Implicitly prunes clusters and states with no assigned data samples.

    Main Results:

    • The proposed algorithm automatically determines K and S.
    • Demonstrates superior performance compared to maximum likelihood estimation with model selection techniques.
    • Achieves better results on both synthetic and real-world datasets.

    Conclusions:

    • The variational Bayesian hierarchical EM algorithm offers an effective approach for HMM clustering.
    • Automatic determination of model complexity (K and S) is a key advantage.
    • The method shows promise for advanced time-series data analysis.