Calibrating general posterior credible regions

By Nicholas Syring, Ryan Martin. Biometrika, 2015

137 citations recorded by the source corpus.

Calibration of credible regions derived from under- or misspecified models is an important and challenging problem. In this paper, we introduce a scalar tuning parameter that controls the posterior distribution spread, and develop a Monte Carlo algorithm that sets this parameter so that the corresponding credible region achieves the nominal frequentist coverage probability.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/975fb1ae3f2296e136733ce7dc9d312488eb0959

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pierre Alquier
2. Olivier Catoni
3. Matthieu Lerasle
4. Roberto Imbuzeiro Oliveira

In Statistics and Probability

1. Pierre Alquier
2. Olivier Catoni
3. Matthieu Lerasle
4. Roberto Imbuzeiro Oliveira

Citations in the corpus

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