Pith. sign in

REVIEW 1 cited by

Lower Bounds for the Total Variation Distance Given Means and Variances of Distributions

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.05820 v2 pith:6HNBUT4P submitted 2022-12-12 math.PR cs.ITcs.LGmath.ITmath.STstat.TH

classification math.PRcs.ITcs.LGmath.ITmath.STstat.TH
keywords givenboundsdistancelowermeanstotalvariancesvariation
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

For arbitrary two probability measures on real d-space with given means and variances (covariance matrices), we provide lower bounds for their total variation distance. In the one-dimensional case, a tight bound is given.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. No Free Lunch for Stochastic Gradient Langevin Dynamics

    stat.CO 2024-12 conditional novelty 5.0 of 10

    Subsampling in SGLD does not asymptotically reduce the total computation needed for accurate posterior sampling in typical exponential-family models.

Pith tools