Multivariate data modelling with Copulas in Python
Python package for canonical vine copula trees with mixed continuous and discrete marginals
Matlab toolbox for canonical vine copula trees with mixed continuous and discrete marginals
Inference for Gaussian copula factor models and its application to causal discovery.
Copula fitting in Python.
The repo contains the main topics carried out in my master's thesis on operational risk. In particular, it is described how to implement the so called Loss Distribution Approach (LDA), which is considered the state-of-the-art method to compute capital charge among large banks.
Robust Estimation of Copulas by Maximum Mean Discrepancy
Copula-based Hidden Markov Model (HMM) to decode tactics in a football match. Final project for the Probabilistic Machine Learning course of the MSc degree in Data Science and Artificial Intelligence (DSAI), University of Trieste
Examples of scheduled jobs estimating copulas at www.microprediction.org
In this project, we predict the probability of occurrence of risk in the Indian territory, based on the historical data of precipitation and temperature between the years 1951 - 2015
Portfolio credit risk modeling
Causal discovery from mixed data with missing values.
Multivariate Environmental Statistics (BEE6300) R Code
Discrete checkerboard copula modeling and implementation of new scoring methods pertaining to ordinal and categorical discrete data.
This project creates a Generative AI system to produce high-quality synthetic data using minimal real data. It combines VAEs, Tabular GANs, and Copula-based methods to generate realistic and diverse datasets, helping overcome data shortages, privacy issues, and biases.
This project creates a Generative AI system to produce high-quality synthetic data using minimal real data. It combines VAEs, Tabular GANs, and Copula-based methods to generate realistic and diverse datasets, helping overcome data shortages, privacy issues, and biases.
Gumbel, Clayton, Frank, and Independence copula that use on Weibull distribution.
The Quant Copula Playground is a Shiny application designed for everyone interested in exploring the dependencies between stock returns using various copula models. This application is inspired by seminal works in the field of copulas, particularly "An Introduction to Copulas" by Roger B. Nelsen.
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