Module for statistical learning, with a particular emphasis on time-dependent modelling
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Updated
Apr 14, 2024 - Python
Module for statistical learning, with a particular emphasis on time-dependent modelling
Umbrella package of the 'spatstat' family................
Spatiotemporal epidemic model introduced in the context of COVID-19, ACM TSAS, 2022
Pieces of code that have appeared on my blog with a focus on stochastic simulations.
A Spatio-temporal point process simulator.
sub-package of spatstat containing core functionality for data analysis and modelling
A general framework for learning spatio-temporal point processes via reinforcement learning
PPG (Point Process Generator) is a Reinforcement Learning framework that is able to produce actions by imitating expert sequences.
Code for "Long Horizon Forecasting With Temporal Point Processes", WSDM 2021
Tools for evaluating the goodness of fit of a point process model via the time rescaling theorem
Code and real data for "Counterfactual Temporal Point Processes", NeurIPS 2022
Hidden Markov Hawkes Process - Model for Analyzing Topical Transitions in text based cascades in Social Networks.
Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis
🧠 A decoder of Spike Trains using 🔬 Bayesian, State-Space, 📐 Point-Processes, EM-algorithm, Maximum-likelihood
A method for event correlation detection based on Spatial-Temporal-Textual point process
Determining Impact of Social Media Badges through Joint Clustering of Temporal Traces and User Features.
3D object-based model of braided river deposits (marked point process), an open-source software package (R language)
Efficient point process inference for large scale object detection
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