We extend conformal prediction methodology beyond the case of exchangeable data.
Methodology
The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol sequences.
Applications Computation
Then, we propose a Bayesian alternative to the synthetic control method that preserves the main features of the standard method and provides a new way of doing valid inference.
Methodology Econometrics
Evaluating forecasts is essential to understand and improve forecasting and make forecasts useful to decision makers.
Methodology Applications Computation
In this paper we introduce a metric aimed at helping machine learning practitioners quickly summarize and communicate the overall importance of each feature in any black-box machine learning prediction model.
Methodology
Nevertheless, the use of additional instruments may be optimal from the perspective of mean squared error even if they are slightly invalid; a small bias in estimation may be a price worth paying for a larger reduction in variance.
Methodology
On the underlying precision matrix of the transformed variables, we consider a spike-and-slab prior and use an efficient posterior Gibbs sampling scheme.
Methodology
Hamiltonian Monte Carlo (HMC) is a popular sampling method in Bayesian inference.
Methodology Computation
Structural reliability analysis is concerned with estimation of the probability of a critical event taking place, described by $P(g(\textbf{X}) \leq 0)$ for some $n$-dimensional random variable $\textbf{X}$ and some real-valued function $g$.
Computation Methodology
To do this, we need to compare the performance of competing models with each other in prospective forecasting mode, and to rank their performance using a fair, reproducible and reliable method.
Applications
