Tutorials
Bayesian Modelling and Probabilistic Programming with Numpyro, and Deep Generative Surrogates for Epidemiology
This tutorial introduces exploring a range of topics in Bayesian modelling, such as Bayesian inference, hierarchical modelling, Gaussian processes for spatial statistics, ordinary differential equations (ODEs), and agent-based models (ABMs) for disease transmission modelling using NumPyro.
Learning with Temporal Point Processes
This tutorial provides an introduction to the basic theory of temporal point processes, revisits several types of point processes, and introduces advanced concepts. This explains how temporal point processes have been used in developing a variety of recent machine learning models and control algorithms, respectively. This tutorial also visits recent advances related to deep learning and Bayesian nonparametrics.
NYC Motor Vehicle Collisions
This tutorial explores motor vehicle collision data in New York City with the goal of identifying trends and patterns that could inform traffic safety measures and urban planning. It leverages data visualization tools, such as heatmaps and time-series plots, to analyze collision distributions across time and space and to highlight high-risk zones. The dataset used includes detailed records of motor vehicle collisions, providing information on dates, times, locations, contributing factors, and the impact on individuals involved, including injuries and fatalities.
https://www.kaggle.com/code/skhiearth/nyc-motor-vehicle-collisions
Neural TPP Model
This tutorial introduces Neural Temporal Point Processes, it talks about how to parameterize TPPs with Neural Networks, derives the likelihood function for Neural TPPs and demonstrates how to train a model in PyTorch with the given loss function based on the likelihood derived.
PyMC Spatial GP
This tutorial from PyMC Labs demonstrates how to model spatial data using Gaussian Processes in PyMC. It walks through defining spatial kernels, modeling spatial variation with a latent GP, and performing posterior predictive checks. It’s ideal for geostatistics, spatial epidemiology, and environmental modeling.
https://www.pymc-labs.com/blog-posts/spatial-gaussian-process-01/
Sentinelsat Package
This tutorial explores the use of the Sentinelsat Python package for using different satellites for retrieving various forms of data, such as all weather data, land-surface temperature, and ocean color and land color with longitude and latitude locations.
Working With Spatio-Temporal Data in Python
This tutorial introduces Python for analyzing and visualizing spatial-temporal data. And uses datasets from the environmental sciences that are freely available.