The codes/files accompanying the GP modelling of yield curve are given below:
This repository contains Jupyter Notebook files related to Bayesian regression problems on real estate dataset. These notebooks explore different techniques and approaches for Bayesian regression modeling, as given below:
- Part A (2 'x' variables): Bayesian Regression with SVI (Diagonal Covariance)
- Part B (6 'x' variables): Bayesian Regression with SVI (Diagonal Covariance)
- Part A: Bayesian Regression with SVI (Multi-dimensional HMC)
- Part B: Bayesian Regression with SVI (Multi-dimensional HMC)
Feel free to explore these notebooks to dive deeper into Bayesian regression and its various implementation techniques.
A quick 20 min introduction to various UQ methods for Deep Learning:-
- Why is UQ required for Deep Learning
- Bayesian NN
- Monte Carlo Dropout
- MCMC
- Variational Inference
- Laplace Approximation
- Deep Ensembles
- Deep Evidence Regression