We present a new approach to semiparametric inference using corrected
po...
Introduction: Increasing interest in real-world evidence has fueled the
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A Neural Process (NP) estimates a stochastic process implicitly defined ...
Causal inference from observational data can be viewed as a missing data...
Bayesian methods are a popular choice for statistical inference in small...
Bayesian nonparametric methods are a popular choice for analysing surviv...
We develop scalable methods for producing conformal Bayesian predictive
...
The prior distribution on parameters of a likelihood is the usual starti...
In Bayesian statistics, the marginal likelihood, also known as the evide...
Increasingly complex datasets pose a number of challenges for Bayesian
i...