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Paper Citation Record · LEDGER

The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1607.03188.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1607.03188 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:36:35.718440Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-14T18:29:22.685137Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f22571de-83f3-4439-995d-ef98095cada7 · inbound

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone cites this paper.

The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-04T07:36:35.718440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T07:36:35.718440Z digest=sha256:6c304effb0e3c273fad3b2a32c91d9ac65fdac5a612ce2f1ff645929c20a1d43

Observation f6bbed42-e84b-47a8-90e4-b184b7fb3a27 · inbound

State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives cites this paper.

State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data

Reference 207

Resolution
verified exact
arxiv_id, observed 2026-05-14T18:29:22.688107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-14T18:27:47.980646Z digest=sha256:1f5bfeb1fc416ac323f4fdf4b5f7616569be8d05f1e85042c9fba707cd3bb831