The NHMC-AR model is a Non-Homogeneous Markov Chain AutoRegressive model. It is designed to perform context-sensitive forecasting in time series that are associated with event sequences.
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Updated
Sep 13, 2023 - Python
The NHMC-AR model is a Non-Homogeneous Markov Chain AutoRegressive model. It is designed to perform context-sensitive forecasting in time series that are associated with event sequences.
Some implementations of partial rejection samplers
Codebase for generating visuals with dynamical systems over cyclic rings.
An implementation of a differentiable point process and a differentiable spiking neural network.
Code for "Survival Permanental Processes for Survival Analysis with Time-Varying Covariates" at NeurIPS2023
Superposition of n i.i.d. one dimensional point processes according to " On the Superposition of Renewal Processes" D. R. Cox and Walter L. Smith
Temporal point process models for time-limited coupon prediction (KDD 2017).
A LSTM based adversarial learning framework for anomaly detection.
Sub-package of spatstat containing code for linear networks
A novel general non-stationary point process model based on neural networks.
Sub-package of spatstat containing functions for random generation
A diffusion-based framework for spatio-temporal point processes
Code and data for "Stochastic Optimal Control of Epidemic Processes in Networks", ML4H at NeurIPS 2018
Compute structure factor of stationary and isotropic point processes
Code for "An Online Algorithm to Reduce the Spread of Misinformation in Social Networks", WSDM 2018
Code for "Hierarchical Dirichlet-Hawkes process: generative model and inference algorithm", WWW 2017
Code and data for "Deep Reinforcement Learning of Marked Temporal Point Processes", NeurIPS 2018
Pieces of code that have appeared on my blog with a focus on stochastic simulations.
Code and real data for "Enhancing Human Learning via Spaced Repetition Optimization", PNAS 2019
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