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Found 10 papers, 6 papers with code
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
Amortized Rejection Sampling in Universal Probabilistic Programming
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
Efficient Bayesian Inference for Nested Simulators
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs.
Generating Material Maps to Map Informal Settlements
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals.
Mapping Informal Settlements in Developing Countries with Multi-resolution, Multi-spectral Data
We propose two effective methods for detecting and mapping the locations of informal settlements.
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).
