arXiv:2606.07901v1

Ergodic Theory in Classical and Bayesian Inference

Artur O. Lopes

math.STmath.DSmath.PR37D3562F0262F1262E2037A5037A10

Abstract

We begin by presenting the mathematical rationale underlying classical deductive inference. We then introduce the foundational ideas of the Bayesian inference framework. Results lying at the interface of Statistics and Ergodic Theory are outlined, providing a theoretical framework applicable to the prediction and analysis of real-world phenomena from random data. This text is expository in nature - no new results are presented; rather, recently published results are described in a didactic manner. Throughout, we work with Hölder equilibrium measures, which encompass a substantially more general class of processes than i.i.d. ones.

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Audit summary

Audited against arXiv v1

Not a correctness certificate. A “Correct” result may include yellow typos or minor formal corrections that do not affect substantive soundness. It means this audit found no unresolved substantive error under the stated criteria; it does not replace expert scrutiny or formal verification.

Current report

Detailed mathematical audit

Generated August 18, 2026
01Statements3 reported findingsContains wrong statements

The paper's principal Bayesian-convergence claim is false without an identifiability hypothesis, and two displayed information-theoretic formulas have the wrong expression or sign. These are substantive mathematical errors, not typographical boundary issues.

Bayesian posterior convergence theoremIncorrect

Posterior concentration is false without identifiability

Pages 6–9 · Bayesian inference section and posterior-convergence conclusion · arXiv:2606.07901v1

The theorem asserts concentration at the true parameter but assumes no condition ensuring that different parameters determine different equilibrium measures. Take a constant family μθ=μθ0\mu_\theta=\mu_{\theta_0} for every θ\theta. Every likelihood ratio is then one, so the posterior equals the prior for every sample size and cannot converge to δθ0\delta_{\theta_0} unless the prior was already that point mass. More generally, cohomologous normalized potentials can determine the same Gibbs measure. An identifiability assumption, together with uniform separation away from θ0\theta_0, is necessary.

Equation (6)Incorrect

The displayed formula is not Kullback–Leibler divergence

Page 4 · Equation (6) · arXiv:2606.07901v1

The paper writes a difference of p0logp0\sum p_0\log p_0 and pθlogp0\sum p_\theta\log p_0. The Kullback–Leibler divergence is instead D(p0pθ)=ip0(i)logp0(i)pθ(i).D(p_0\|p_\theta)=\sum_i p_0(i)\log\frac{p_0(i)}{p_\theta(i)}. The printed expression can be negative: for p0=(0.9,0.1)p_0=(0.9,0.1) and pθ=(1,0)p_\theta=(1,0), it is approximately 0.220-0.220, whereas a divergence is nonnegative.

Equation (15)Incorrect

The asymptotic likelihood-ratio sign is reversed

Page 8 · Equation (15) and its use in the posterior estimate · arXiv:2606.07901v1

Under data distributed according to μθ0\mu_{\theta_0}, the normalized log likelihood ratio satisfies 1nlogμθ(Cn)μθ0(Cn)D(μθ0μθ)0,\frac1n\log\frac{\mu_\theta(C_n)}{\mu_{\theta_0}(C_n)}\longrightarrow -D(\mu_{\theta_0}\|\mu_\theta)\leq 0, not the positive rate printed in the paper. This sign error reverses the exponential behavior used in the subsequent normalization argument.

02Proofs1 reported findingContains incorrect or incomplete proofs

The posterior-convergence argument relies on the false divergence formula, the reversed likelihood-ratio sign, and an unproved uniform separation that is impossible in nonidentifiable families. The proof cannot be repaired without adding hypotheses and rewriting the central estimate.

Equations (10), (11) and (15)Incorrect as written

The exponential posterior estimate does not follow

Pages 6–9 · arXiv:2606.07901v1

Equations (10)–(11) inherit the incorrect weighting from Equation (6), and the displayed bound uses an infimum where a uniform upper bound over parameters away from θ0\theta_0 is required. Equation (15) then supplies the wrong sign. Even after correcting the algebra, pointwise convergence of likelihood ratios does not by itself justify a uniform integral bound over the parameter space. A valid proof would need identifiability, compactness or tightness, and a uniform large-deviation or continuity argument.

03Novelty0 reported findingsNo non-novelty findings

No non-novelty findings.

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Paper
arXiv:2606.07901v1
Authors listed
Artur O. Lopes
Audit date
August 18, 2026
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