6 citations · 10 across the 12 of their papers we have counts for
4 papers · 1 filter
Improved dimension dependence in the Bernstein von Mises Theorem via a new Laplace approximation bound
Anya Katsevich
The Bernstein-von Mises theorem (BvM) gives conditions under which the posterior distribution of a parameter based on independent samples is asympto…
The Laplace approximation accuracy in high dimensions: a refined analysis and new skew adjustment
Anya Katsevich
In Bayesian inference, making deductions about a parameter of interest requires one to sample from or compute an integral against a posterior distribution. A popular method to make…
Tight Bounds on the Laplace Approximation Accuracy in High Dimensions
Anya Katsevich
In Bayesian inference, a widespread technique to compute integrals against a high-dimensional posterior is to use a Gaussian proxy to the posterior known as the Laplace approximati…
On the Approximation Accuracy of Gaussian Variational Inference
Anya Katsevich, Philippe Rigollet
The main computational challenge in Bayesian inference is to compute integrals against a high-dimensional posterior distribution. In the past decades, variational inference (VI) ha…