Hierarchical Likelihood
- Incorporates prior information into likelihood calculations to inform parameter estimation.
- Particularly useful when data are limited or uncertain, improving prediction or estimation accuracy.
- Applied in contexts such as disease outbreaks and consumer behavior analysis.
Definition
Section titled “Definition”Hierarchical likelihood is a statistical method that allows for the incorporation of prior knowledge or information into the likelihood calculation.
Explanation
Section titled “Explanation”Hierarchical likelihood augments the standard likelihood framework by including external or prior information in the likelihood calculation. This approach is employed when there is uncertainty or lack of data, using prior knowledge to stabilize estimates and produce more accurate predictions or estimates than would be possible from the observed data alone.
Examples
Section titled “Examples”Disease outbreaks
Section titled “Disease outbreaks”Consider a new virus spreading rapidly in a region. Researchers collect data on the number of cases in each city but face uncertainty about the true transmission rate. Hierarchical likelihood can incorporate prior knowledge about the transmission rate from similar viruses, allowing for more accurate predictions about the spread of the disease.
Consumer behavior
Section titled “Consumer behavior”A marketing company wants to predict the likelihood that a customer will make a purchase based on factors such as age and income, but there is uncertainty about the true relationship between these factors and purchasing behavior. Hierarchical likelihood can incorporate prior knowledge about consumer behavior from previous studies, enabling more accurate predictions about customer purchasing behavior.
Use cases
Section titled “Use cases”- Disease outbreaks
- Consumer behavior
Related terms
Section titled “Related terms”- Likelihood
- Prior knowledge