Bayesian Hierarchical Prediction
Current Research
My current research is in developing Bayesian Hierarchical Prediction Models for Complex Systems. Bayesian hierarchical prediction is a statistical model that uses Bayesian methods to estimate parameters in a hierarchical model. It is used to make predictions in complex systems by combining sub-models to account for uncertainty.
Features:
- Hierarchical structure - The model is written in multiple levels, or a hierarchy, to represent the relationships between different levels of data.
- Sub-models - The model is made up of sub-models that are combined to form the hierarchical model.
- Bayes' theorem - Bayes' theorem is used to integrate the sub-models with observed data.
- Posterior distribution - The result of the integration is the posterior distribution, which is an updated probability estimate.
Uses:
- Complex systems - Bayesian hierarchical models are useful for modeling complex interacting systems.
- Limited data - Hierarchical models can estimate results for lower levels of a hierarchy, even when there is limited data.
- Prior sensitivity - Hierarchical models can help mitigate the sensitivity of Bayesian models to the choice of prior distribution.
Applications:
- Predicting Product Defects in Manufacturing Processes.
- Ticket Sales – Predicting Seats Sold and Revenue for Entertainment Events.
- Detecting fraudulent transactions or assessing the creditworthiness of customer.