Edris Loftpouri MFE /24 discusses his interest on the implementation of Bayesian Neural Networks (BNNs) for macroeconomic forecasting. He also touches on Castastrophe Modeling


https://www.linkedin.com/in/patrick-z-08bb5b5a/

https://www.linkedin.com/company/lehigh-master-in-financial-engineering/

This project develops a Bayesian Neural Network (BNN) for macroeconomic forecasting, using stochastic volatility and Bayesian shrinkage priors to manage complex, high-dimensional data. With layer-specific and neuron-specific activation functions, the model captures both long-term dependencies and short-term nonlinear dynamics. Offering adaptive uncertainty quantification and robust volatility handling, it’s ideal for risk analysis, economic policy, and quantitative finance applications.



https://www.linkedin.com/in/edris-lotfpouri/

Bayesian Neural Networks

Damiano Brigo, chair of mathematical finance at Imperial College London, shares his thoughts on the lost causes, the present role and the future prospects of quantitative finance.

Damiano Brigo – 22/01/18

In this podcast excerpt, Ritwik Gupta and Carson Sestili describe deep learning and how it differs from machine learning.  “As you compose more and more non-linear functions together, you can represent a much wider function space than you could with just one non-linear function. That is why deep learning is different from shallow learning. Shallow learning doesn’t compose multiple things together. Deep learning does.”

Deep Learning in Depth: Deep Learning versus Machine Learning

Created by Professor Patrick Zoro The podcast aims to capture the latest trends in Data analytics, Asset Management, Blockchain, Risk Management.

Patrick Zoro is also the program manager of the Master of Financial Engineering program at Lehigh University

https://cbe.lehigh.edu/academics/graduate/master-analytical-finance

Bayesian Neural Networks

The Quant / Financial Engineering Podcast

Related tracks