Paper Citation Record · LEDGER

Learning Ergodic Dynamical Systems from a Finite Trajectory

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.22399.

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

pith.paper-citation-record.v1
2607.22399 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:58:41.837117Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

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unresolved20
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External citation measurements

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Outbound references

Observation 4f2c8fda-df62-4efb-b653-ec77a8f2fdac · outbound

This paper cites an unresolved cited work.

Learning Ergodic Dynamical Systems from a Finite Trajectory Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:39.520881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b84fdc2d-ac38-4fa9-9432-61fbdf59f305 · outbound

This paper cites Fischer, S.

Learning Ergodic Dynamical Systems from a Finite Trajectory Fischer, S

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:39.848581Z

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Unavailable: canonical work link unavailable.

Observation 528dee93-de8b-4147-92b1-4be93fda8070 · outbound

This paper cites Modelling transition dynamics in MDPs with RKHS embeddings.

Learning Ergodic Dynamical Systems from a Finite Trajectory Modelling transition dynamics in MDPs with RKHS embeddings

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:39.920954Z

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Observation 4b9bd2bb-d8f4-481b-8fd6-8683d465d371 · outbound

This paper cites If the conditional probabilities in(85) do not depend ont, the process is calledtime-homogeneous.

Learning Ergodic Dynamical Systems from a Finite Trajectory If the conditional probabilities in(85) do not depend ont, the process is calledtime-homogeneous

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.758807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3b0829-b6e2-4f9a-84cf-ead1e6d4af04 · outbound

This paper cites are mutually independent, then P-almost everyω∈Ωdetermines a sequence xt =X t(ω), η t =N t(ω), satisfying (87).

Learning Ergodic Dynamical Systems from a Finite Trajectory are mutually independent, then P-almost everyω∈Ωdetermines a sequence xt =X t(ω), η t =N t(ω), satisfying (87)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.949041Z

Source-reported events for the cited work

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Observation 70d47e31-a97b-40de-a7a9-7dc6ed7c1ece · outbound

This paper cites are mutually independent,X0 has law(ξ0)#PX, and eachNt has lawξη #PN.

Learning Ergodic Dynamical Systems from a Finite Trajectory are mutually independent,X0 has law(ξ0)#PX, and eachNt has lawξη #PN

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.064500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed73a524-b073-4be4-9d3a-7bf6be70a301 · outbound

This paper cites (90) Let Gt = { σ(X0), t= 0, σ(X0,N 0,...,N t−1), t≥1.

Learning Ergodic Dynamical Systems from a Finite Trajectory (90) Let Gt = { σ(X0), t= 0, σ(X0,N 0,...,N t−1), t≥1

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.221891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d311b8e-c31c-4abf-894b-3cee6acc963c · outbound

This paper cites SinceT is a measurable isomorphism onto its image˜Y, equality holds.

Learning Ergodic Dynamical Systems from a Finite Trajectory SinceT is a measurable isomorphism onto its image˜Y, equality holds

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.318042Z

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Observation 05ee6a04-f27f-4ad6-92d9-4b26f2df890c · outbound

This paper cites P(x,A) = ∫ A 1 (2πσ 2)d/2 exp ( −∥x′−b(x)∥ 2 X 2σ2 )    p(x,x′) dx′, x∈X,A∈B(X).

Learning Ergodic Dynamical Systems from a Finite Trajectory P(x,A) = ∫ A 1 (2πσ 2)d/2 exp ( −∥x′−b(x)∥ 2 X 2σ2 )    p(x,x′) dx′, x∈X,A∈B(X)

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.468921Z

Source-reported events for the cited work

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Observation 4a961c54-7b21-40d6-bbfc-68d4a8774b6b · outbound

This paper cites It is positive since for allf∈H ⟨Σρf,f⟩H = ∫ |⟨f,Φ(x)⟩H|2ρ(dx)≥0.

Learning Ergodic Dynamical Systems from a Finite Trajectory It is positive since for allf∈H ⟨Σρf,f⟩H = ∫ |⟨f,Φ(x)⟩H|2ρ(dx)≥0

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.551854Z

Source-reported events for the cited work

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Observation 821f9e7a-03bb-447f-bdcc-68a5e940def1 · outbound

This paper cites See, e.g., Douc et al.

Learning Ergodic Dynamical Systems from a Finite Trajectory See, e.g., Douc et al

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.719065Z

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Observation 55685388-ac81-46a5-b177-070aa6c00751 · outbound

This paper cites Consequently,MT is a martingale inHwith respect toF.

Learning Ergodic Dynamical Systems from a Finite Trajectory Consequently,MT is a martingale inHwith respect toF

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:41.837117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed960f61-cbbe-417b-8187-7782fa242d92 · outbound

This paper cites Online Learning for Nonlinear Dynamical Systems without the I.I.D. Condition.

Learning Ergodic Dynamical Systems from a Finite Trajectory Online Learning for Nonlinear Dynamical Systems without the I.I.D. Condition

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.666452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3865352a-c52f-4702-860e-4e84bfcfc66a · outbound

This paper cites an unresolved cited work.

Learning Ergodic Dynamical Systems from a Finite Trajectory Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.418121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c95a0974-c644-4873-a3cb-e82e967e771e · outbound

This paper cites Modern Koopman Theory for Dynamical Systems.

Learning Ergodic Dynamical Systems from a Finite Trajectory Modern Koopman Theory for Dynamical Systems

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:39.684574Z

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Observation 52012ad6-465d-46b8-aaae-eb349c0feb7b · outbound

This paper cites On the Consistency of Kernel Methods with Dependent Observations.

Learning Ergodic Dynamical Systems from a Finite Trajectory On the Consistency of Kernel Methods with Dependent Observations

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.155834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5f3dd56-c483-47db-a6a9-5c105799b79f · outbound

This paper cites Theory and Algorithms for Forecasting Time Series.

Learning Ergodic Dynamical Systems from a Finite Trajectory Theory and Algorithms for Forecasting Time Series

Reference 156

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:39.989678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a8b3416-0d4f-4d16-857b-10a4b8596780 · outbound

This paper cites an unresolved cited work.

Learning Ergodic Dynamical Systems from a Finite Trajectory Unresolved cited work

Reference 204

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.301376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89039fa2-85cd-4641-acfa-cabca907443f · outbound

This paper cites I., and Recht, B.

Learning Ergodic Dynamical Systems from a Finite Trajectory I., and Recht, B

Reference 423

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.532557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 156e6100-1d19-4ce0-9a05-f74608a14a11 · outbound

This paper cites Thus the pair(µ0,P )determines the law of the processuniquely, although the process may admit many realizations on different probability spaces.

Learning Ergodic Dynamical Systems from a Finite Trajectory Thus the pair(µ0,P )determines the law of the processuniquely, although the process may admit many realizations on different probability spaces

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-01T04:58:40.856185Z

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Unavailable: canonical work link unavailable.

Pith citing papers

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