27-29 November, Vilnius
Conference about Big Data, High Load, Data Science, Machine Learning & AI
Early Bird Ends In:
Amir Meimand is Zilliant Director of R&D, pricing scientist, where he designs and develops pricing solutions for customers and performs research in which he applies new methods to improve the current solutions as well as develop new tools. Prior to joining Zilliant, Amir helped design and develop a promotion planning and pricing platform for B2C retailers.
Amir holds a dual Ph.D. degree in Industrial Engineering and Operations Research from Pennsylvania State University. In his doctoral work, he applied operations research concepts to dynamic pricing and revenue management.
Bayesian Hierarchical Model for Predictive Analysis
Many predictive models require data to be structured (e.g., demographically, spatially, temporally). Each group of structured data has it is own parameters, but these parameters are related because data have a natural hierarchical structure. In such cases, two modeling approaches may be taken:
- All data may be modeled collectively;
2. Each group of data may be modeled separately.
With the first approach, there is a risk of ignoring both autocorrelation and latent differences, while with the second approach, there is a risk of ignoring latent similarity and data sparsity can be problematic. The best approach in such a situation is to develop a hierarchical model with the flexibility to capture and analyze the data structure, and the ability to account for and estimate effects from different groups. Hierarchical models are stronger because data can be analyzed across groups, thereby minimizing the effect of data sparsity. Higher level information can be shared effectively among the lower level groups, yet lower level estimation still follows its own data structure and pattern.
In this workshop we focus on the application of Bayesian hierarchical linear regression model in the area of pricing and revenue management. We will discuss how multi-layer model can be applied to hierarchal dataset to deal with data sparsity and reduce the noise to provide reliable and robust prediction. In this use case the behavior of every individual is modeled as a linear regression assuming each individual has its own unique behavior while there are some similarities in the behavior of the same groups.
Hierarchical Data Structure 45 mins:
- How to define and/or detect hierarchy
- The application of Hierarchical structure in prediction
Bayesian Concept 45 mins:
- Bayesian vs. Frequentist-Similarities, differences and Applications
- Bayesian Regression Models
- Pymc python library to develop a simple Bayesian model
Practical workshop (Part 1) 30 mins:
- Develop a predictive model for ‘Flight Delay’ based on Bayesian model
Bayesian Hieratical Approach 30 mins:
- Why do we need this combination?
- When do we need this combination?
- Real world application
Practical workshop (Part 2) 1 hour:
- Enhance the ‘Flight Prediction’ model with Bayesian hieratical model
Bayesian Hieratical Model in Pricing and Revenue Management 45 mins
During the course you will learn the concept and application of hieratical Bayesian model in real world. In this workshop we will go through variety of analytical techniques to identify and detect the hierarchical structure of real world data set. The we will discuss about how and why we need to combine the hierarchical structure with Bayesian model to enhance and impower a predive model by sharing the information more effectively. Suring this workshop the audience will learn about pymc library of python to develop a Bayesian Hierarchicalmodel from starch
Level 2 – Intermediate material. Assumes knowledge and provides specific details about the topic.
A personal computer with Python 3 and Jupyter Notebook