Paper Citation Record · LEDGER

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression

As of 9 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2606.06567.

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pith.paper-citation-record.v1
2606.06567 v1

Coverage vector

measured 44 of 44 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

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External citation measurements

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Outbound references

Observation d20ae8bd-7682-47ad-8233-a0953a496880 · outbound

This paper cites an unresolved cited work.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

Reference 1

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This paper cites Kronberger, B.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Kronberger, B

Reference 2

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This paper cites Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods,

Reference 3

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This paper cites Pawitan,In all likelihood: statistical modelling and inference using likelihood, paperback ed.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Pawitan,In all likelihood: statistical modelling and inference using likelihood, paperback ed

Reference 4

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Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

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This paper cites Variable selection for joint mean and dispersion models of the inverse Gaussian distribution,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Variable selection for joint mean and dispersion models of the inverse Gaussian distribution,

Reference 6

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Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

Reference 7

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This paper cites Nocedal and S.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Nocedal and S

Reference 8

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Observation 0da67dab-2cbb-4e3b-bf27-54926169ce2f · outbound

This paper cites The Limiting Distributions of Certain Statistics,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression The Limiting Distributions of Certain Statistics,

Reference 9

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Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

Reference 10

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This paper cites Conformal Prediction: A Gentle Introduction,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Conformal Prediction: A Gentle Introduction,

Reference 11

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This paper cites Gelman, J.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Gelman, J

Reference 12

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This paper cites MCMC algorithms for constrained variance matrices,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression MCMC algorithms for constrained variance matrices,

Reference 13

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This paper cites Adaptive Rejection Metropolis Sampling within Gibbs Sampling,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Adaptive Rejection Metropolis Sampling within Gibbs Sampling,

Reference 14

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This paper cites Fractional Bayes Factors for Model Comparison,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Fractional Bayes Factors for Model Comparison,

Reference 15

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This paper cites Model Selection and the Principle of Minimum Description Length,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Model Selection and the Principle of Minimum Description Length,

Reference 16

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This paper cites Modeling by shortest data description,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Modeling by shortest data description,

Reference 17

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Observation 46d83734-ef98-4499-ae9c-fb227871ac4e · outbound

This paper cites Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression,

Reference 18

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This paper cites Au- tomated learning of interpretable models with quantified uncertainty,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Au- tomated learning of interpretable models with quantified uncertainty,

Reference 19

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Observation 93ff58a1-0665-4f10-828e-d03ebacb20a8 · outbound

This paper cites Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Comparing Methods for Estimating Marginal Likelihood in Symbolic Regression,

Reference 20

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This paper cites Bayesian symbolic regression via posterior sampling,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Bayesian symbolic regression via posterior sampling,

Reference 21

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This paper cites Priors for symbolic re- gression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Priors for symbolic re- gression,

Reference 22

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This paper cites A Bayesian machine scientist to aid in the solution of challenging scientific problems,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression A Bayesian machine scientist to aid in the solution of challenging scientific problems,

Reference 23

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This paper cites Bayesian symbolic regression: auto- mated equation discovery from a physicist’s perspective,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Bayesian symbolic regression: auto- mated equation discovery from a physicist’s perspective,

Reference 24

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This paper cites Estimation of probabilities from sparse data for the language model component of a speech recognizer,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Estimation of probabilities from sparse data for the language model component of a speech recognizer,

Reference 25

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This paper cites Exhaustive Symbolic Regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Exhaustive Symbolic Regression,

Reference 26

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This paper cites Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors

Reference 27

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This paper cites The Inefficiency of Genetic Programming for Symbolic Regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression The Inefficiency of Genetic Programming for Symbolic Regression,

Reference 28

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This paper cites Ensemble Bayesian Model Averaging in Genetic Programming,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Ensemble Bayesian Model Averaging in Genetic Programming,

Reference 29

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This paper cites Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Probabilistic Symbolic Regression for Equation Discovery via Operator-induced and Regularized Symbolic Forests

Reference 30

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Observation 275d3879-727d-4359-a3a2-43b585c259e1 · outbound

This paper cites VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression VaSST: Variational Inference for Symbolic Regression using Soft Symbolic Trees

Reference 31

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This paper cites An Introduction to Variational Autoen- coders,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression An Introduction to Variational Autoen- coders,

Reference 32

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Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

Reference 33

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Observation d8679de7-e4bc-4e7d-b0a6-9b40e70df4ad · outbound

This paper cites Uncertainty quantification based on symbolic regression and probabilistic programming and its application,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Uncertainty quantification based on symbolic regression and probabilistic programming and its application,

Reference 34

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This paper cites Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantiles,

Reference 35

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Observation a5e98c64-690c-4dd0-bb42-7d133ed3d410 · outbound

This paper cites Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles

Reference 36

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Observation e24c71a0-fe76-4e9d-9990-1b42fe60f998 · outbound

This paper cites Discovering Unmodeled Components in Astrodynamics with Symbolic Regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Discovering Unmodeled Components in Astrodynamics with Symbolic Regression,

Reference 37

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This paper cites Learning noise,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Learning noise,

Reference 38

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This paper cites Combining conformal prediction and genetic programming for symbolic interval regression,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Combining conformal prediction and genetic programming for symbolic interval regression,

Reference 39

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This paper cites Genetic programming using a minimum description length principle,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Genetic programming using a minimum description length principle,

Reference 40

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This paper cites Regularization approach to inductive genetic programming,.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Regularization approach to inductive genetic programming,

Reference 41

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This paper cites By fitting the model into the training set and evaluating it into the validation, we get a more reliable unbiased estimation of the likelihood.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression By fitting the model into the training set and evaluating it into the validation, we get a more reliable unbiased estimation of the likelihood

Reference 42

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Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression Unresolved cited work

Reference 43

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Observation 34a7d7b7-4459-4355-a702-d62c9cc459e5 · outbound

This paper cites The complexity penalty is scaled by the logarithm ofN, thus under a large sample regime, models with fewer parame- ters are preferred [7, Sec.

Are you sure? A Comprehensive and Comprehensible Survey of Uncertainty Quantification in Symbolic Regression The complexity penalty is scaled by the logarithm ofN, thus under a large sample regime, models with fewer parame- ters are preferred [7, Sec

Reference 44

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