Our new methods are provably robust, with substantially less loss of coverage when exchangeability is violated due to distribution drift or other challenging features of real data, while also achieving the same coverage guarantees as existing conformal prediction methods if the data points are in fact exchangeable.
The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications.
With this competency, we can better understand data and influence decisions towards appropriate actions.
ruptures is a Python library for offline change point detection.
Computation Mathematical Software
The online sports gambling industry employs teams of data analysts to build forecast models that turn the odds at sports games in their favour.
Applications Other Computer Science Other Statistics
Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code.
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory.
This paper discusses desirable properties of forecasting models in production systems.
We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges.
StateSpaceModels. jl is an open-source Julia package for modeling, forecasting and simulating time series in a state-space framework.
Computation Optimization and Control