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Hellinger stability and posterior discretization — Examples and Counterexamples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Bayesian models, priors, and posterior laws
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- Complexification, Realification and Real Structures
- Conditional Distributions and Regular Conditional Probability
- Conditional Expectation
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Countability Axioms and Cardinal Functions
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Domination and Likelihood Ratios
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability Spaces and Random Variables
- Foundations of the Real Numbers for Analysis
- Function-space priors and sample regularity
- Hellinger geometry and posterior consistency
- Hellinger stability and posterior discretization
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Infinite Product Measures and Kolmogorov Extension
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Inverse maps, identifiability, and stability
- Lebesgue-Stieltjes Measures and Distribution Functions
- Likelihood potentials and posterior propriety
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Loss, Risk and Decision Theory
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Outward rounded interval and ball arithmetic
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Posterior propriety and dominated Bayes formula
- Power Series and Real-Analytic Functions
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Separation Axioms: the Hierarchy
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Sigma Algebras and Borel Sets
- Signed and Complex Measures Hahn and Jordan
- Simple Field Extensions and the Construction of the Complex Numbers
- Standard-Borel Real Codings and Determining Classes
- Stationary Markov Chains and Ergodic Limits
- Statistical Experiments, Models and Identifiability
- Subspaces, Products, and Quotients
- Suprema and Infima
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Derivative and the Mean Value Theorems
- The Exponential Function
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Radon Nikodym Theorem and Lebesgue Decomposition
- The Riemann Integral: Definition and Integrability
- The Topology of Euclidean Space
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Weak Convergence Tightness and Representation
2 · Logical flowchart
3 · Definitions, theorems and proofs
None yet.
4 · Examples, counterexamples and false statements
Equal posterior means do not control the full posterior
Statement refuted
For any proper prior probability measure on a measurable real-valued parameter space and any two finite likelihood potentials with positive finite evidence and finite posterior means, equality of the two posterior means implies equality of the posterior laws and hence zero Hellinger distance.
Statement references
A measurable space is a set equipped with a sigma-algebra.
A probability measure has total mass one.
The Borel sigma-algebra is generated by the open sets.
The mean of an integrable real random variable is its probability integral.
On a finite probability space, expectation is the finite weighted sum.
Facts & Assumptions
Given: The false statement above, the fixed-data potential update convention, and the library root-L2 Hellinger convention.
For a finite potential on a prior space, the evidence is and the posterior density relative to the prior is whenever . Misfit evidence and the potential posterior law
If have supplied densities with respect to a common measure , then . Root-L2 Hellinger distance for dominated laws
The natural logarithm is the inverse of the real exponential on . The natural logarithm as the inverse of the exponential function
A measurable space is a set equipped with a sigma-algebra, and a probability measure is a countably additive measure with total mass one. Measurable spaces and measurable sets, Probability measures and probability spaces
A posterior mean of an integrable real parameter is its expectation; on this finite space it is the finite probability-weighted sum. Expectation of a nonnegative or integrable random variable, Expectation of a real random variable on a finite probability space
For any , the intervals , indexed by , cover and omit ; hence is open and is closed. Thus is Borel, every subset of the finite set is Borel, and its trace Borel sigma-algebra is the power set; every map from to a measurable target is consequently measurable. The Borel sigma-algebra of a topological space
Counterexample
Take , with its Borel subspace structure, and the prior that assigns mass to each point. Put and define two probability mass functions on by Define finite potentials and . Their evidences are both one, so their posterior updates are precisely the laws with mass functions and .
The uniform prior is the finite atomic measure for ; it is countably additive on this finite space and has , so [F4] makes it a probability measure. The masses of and are positive and each sums to one. By [F6], is Borel and both finite potentials are measurable. By [F3], and for every . Thus and . By [F1], the two posterior updates have masses and ; they are proper and have finite means.
By [F5], their means are and , respectively. Hence their posterior means agree.
Using as the common dominating measure, the corresponding densities are and . By [F2], Since , the central term is greater than . Since and , each endpoint squared difference is greater than . Therefore . Equal posterior means thus coexist with Hellinger distance greater than one.
Remarks
This finite example only refutes a mean-only conclusion. It makes no claim that the two potentials satisfy the Hellinger stability or discretization hypotheses on the A page.
A stable two-point potential gives a Lipschitz posterior
Example
Let , , and let be the atomic Borel probability on given by for Borel . For each fixed datum , use the finite potential . Its evidence and posterior mass at are For every , this potential satisfies the weighted envelope and data-Lipschitz assumptions of the Hellinger data-stability theorem; hence some satisfies for all . These are pointwise fixed-data updates; no observation law or conditional-law version is asserted.
Example references
A prior is a Borel probability with full mass on its declared forward domain.
The Borel sigma-algebra is generated by the open sets.
A norm induces the metric used on the parameter space.
A Banach space is complete for its norm metric.
A separable space has an at most countable dense subset.
Continuity means inverse images of open sets are open.
The relative topology consists of intersections of ambient open sets with the subspace.
The product topology has a basis of open rectangles.
Every real polynomial is continuous on its domain.
The product sigma-algebra is generated by measurable rectangles.
The extended real line adds two infinite endpoints to the real line.
The extended-real Borel sigma-algebra restricts to the real Borel sets.
A function is measurable when inverse images of measurable sets are measurable.
A fixed-data potential defines evidence and a normalized posterior update on positive finite fibers.
Facts & Assumptions
Given: The two-point prior, finite potential, pointwise evidence/posterior convention, and a data interval with .
The fixed-data posterior density relative to is when . Misfit evidence and the potential posterior law
The data-stability theorem gives a root-L2 Hellinger Lipschitz bound for every pair of data in a bounded ball, and the defining Hellinger distance for the supplied posterior densities is their root-L2 distance. Hellinger stability of function-space posteriors under data perturbations, Root-L2 Hellinger distance for dominated laws
The specified two-atom law is a Borel probability with full mass on the finite Borel set ; are taken with their standard separable Banach structure and norm metric. Probability measures and probability spaces, A Borel prior concentrated on a forward-map domain, The Borel sigma-algebra of a topological space, Banach space, Separability: the existence of an at most countable dense subset, A norm on a real vector space, the induced metric, and the dictionary with the metric axioms
For each fixed , is a continuous real polynomial; the open slices show that is jointly continuous. Every open is with each open in , and each such rectangle belongs to the product sigma-algebra. Thus every open preimage under is product-measurable; since open sets generate the real Borel sets and the extended-real Borel sigma-algebra restricts to the real one, is measurable as an extended-real-valued map. Continuity of a map of topological spaces at a point and globally, Subspace topology: the traces of the open sets, its closed sets and its bases, the continuity of the inclusion, and the characteristic property of a map into a subspace, The product set of functions choosing a point in each factor, the projections, the box topology, and the product topology as the initial topology of the projections; the empty product is a one-point space, Sums, scalar multiples, products, absolute values, maxima, minima and quotients with nonvanishing denominator of continuous functions are continuous, as are constants, the identity and every polynomial function, The product sigma-algebra and its finite iterates, The Borel sigma-algebra of a topological space, The extended real line , its order, and the arithmetic that is left undefined, The Borel sigma-algebra on the extended real line, A measurable function between measurable spaces
Verification
Given: , , the equal-atom prior, and .
For any , the intervals , indexed by , cover and omit ; hence is open and every singleton is closed. Therefore is Borel as a finite union of closed sets, and it is bounded because on . The open intervals and intersect in and , respectively, so every singleton is relatively open and has the discrete relative topology. The displayed finite atomic measure is countably additive and its two atom masses sum to one, so it is a Borel probability with . Thus itself is a bounded Borel subset of positive prior mass, as required by the theorem. The real line is a separable Banach space. By [F4], for any open , intersects each open slice in an open subset of that slice, hence open in ; taking the union over proves joint continuity. The potential is nonnegative. For and , Here by the triangle inequality. Thus choose and , which are Borel and nondecreasing in each argument. Then so [F2] applies and gives the stated Hellinger Lipschitz stability on .
By [F1], the evidence is the displayed two-term sum and is positive. The posterior mass ratio is the two masses sum to one, so . The verified evidence and the explicit formula determine the whole two-point posterior.
A nonintegrable error envelope destroys the linear Hellinger rate
Statement refuted
For every separable real Banach space , Borel set , and Borel probability with and positive mass on a bounded subset of . Give the relative norm topology. Let be finite continuous potentials indexed by integers , let be Borel and nondecreasing, and let tend to zero. If and for every , and their evidence normalizers are positive and finite, then there are and such that the library root-L2 Hellinger distance satisfies for every , even without assuming .
Statement references
The library Hellinger distance is the root-L2 norm of the difference of square-root densities.
A Banach space is complete for its norm metric.
A separable space has an at most countable dense subset.
A norm induces the metric used on the parameter space.
The Borel sigma-algebra is generated by open sets.
A probability measure has total mass one.
The metric topology declares a set open when every point has an open ball inside it.
The relative topology consists of intersections of ambient open sets with the subspace.
Continuity means inverse images of open sets are open.
Facts & Assumptions
Given: A counterexample is required to the no-integrability claim in Statement refuted.
For a fixed-data potential, and the posterior density relative to is on positive finite evidence fibers. Misfit evidence and the potential posterior law
For supplied densities relative to the same measure, the library Hellinger distance satisfies . Root-L2 Hellinger distance for dominated laws
The series converges, while diverges. For rational , converges iff
The valid potential-discretization result assumes to conclude a linear Hellinger rate. Hellinger error bound for posterior potential discretization
The real exponential is continuous and strictly increasing, with and . The exponential function is strictly increasing, The exponential is positive and satisfies
The real line with its absolute-value metric is a separable Banach space. For each , the intervals , indexed by , cover and omit , so is closed. Hence is Borel. Also for each , so every singleton is relatively open, is discrete, and every real-valued function on is continuous. Banach space, Separability: the existence of an at most countable dense subset, A norm on a real vector space, the induced metric, and the dictionary with the metric axioms, The Borel sigma-algebra of a topological space, The metric topology: a set is open when every one of its points has a ball around it inside the set; closed means open complement, Subspace topology: the traces of the open sets, its closed sets and its bases, the continuity of the inclusion, and the characteristic property of a map into a subspace, Continuity of a map of topological spaces at a point and globally
The assigned values of are nonnegative, and the empty set has measure zero by the empty sum. For pairwise disjoint Borel sets , the nonnegative series in the definition of partitions across the disjoint sets , so . By [F3], is finite and , hence is a Borel probability measure with . Probability measures and probability spaces
Counterexample
Let with its usual norm, with the trace Borel sigma-algebra, and . For Borel , define , so . Let , for integers , and set , , and . The potentials have common lower envelope zero and satisfy , but the weighted error envelope is not prior-integrable.
By [F3], , so is finite and positive and [F7] makes a probability with . By [F6], is Borel and has the discrete relative topology, so every real-valued function on , including , is continuous. The real line with its usual norm is a separable Banach space by [F6]. Thus all are finite continuous functions on . They satisfy and . But since the second summand contributes for each . This is precisely the integrability hypothesis used by [F4], and it fails here.
The base evidence is . For each , the approximate evidence is positive and at most one, so both posterior updates exist. Moreover : given , choose with by [F3]. The finite sum tends to zero by continuity of the exponential [F5], while the remaining tail of is at most .
Since , the number is less than one. From , for all sufficiently large we have . For , so .
The posterior density of the base law relative to is , and that of the approximation is . By [F2] and step 3.1, because the block has terms and each in it satisfies . Therefore , while . The ratio is at least and is unbounded, so the claimed rate fails.
Sources
- M. Dashti and A. M. Stuart, The Bayesian Approach to Inverse Problems (full lecture notes, arXiv:1302.6989)
- S. L. Cotter, M. Dashti, and A. M. Stuart, Approximation of Bayesian Inverse Problems for PDEs
- J. Latz, On the Well-posedness of Bayesian Inverse Problems, SIAM/ASA Journal on Uncertainty Quantification 8 (2020)