Generalised Comb Divergences

• Generalised comb divergences are a broad class of parametrically flexible divergence measures that unify classical and extended divergence families through structured aggregation of elementary contributions.
• They leverage parameterized means and quasi-arithmetic constructions to ensure properties such as convexity, duality, and metric validity for robust statistical comparisons.
• Their framework provides explicit operational links to Bayesian risk, maximum entropy estimation, and robust inference, supporting diverse applications from clustering to nonextensive physics.

Generalised comb divergences are a broad class of parametrically and structurally flexible divergence measures designed to quantify differences between probability distributions by aggregating or “combing” together various elementary divergence contributions. This conceptual framework encompasses classical divergences (e.g., Kullback–Leibler, Hellinger, Jensen–Shannon, f-divergences), their parametric and quasi-arithmetic generalizations, and newly constructed multimodal or multi-indexed divergences, enabling a finer, tunable control of statistical dissimilarity. The main scientific advances in the development of generalised comb divergences arise from (a) unifying known divergence families via elementary mean-type constructions, (b) establishing metric, optimization, and duality properties, and (c) providing explicit operational connections—such as with Bayes risk, robustness, and entropy measures—in statistical learning and inference.

1. Foundational Principles and General Construction

Central to generalized comb divergences is the use of parameterized means, convexity/generalized convexity, and variational representations to define a vast landscape of divergence measures. A paradigmatic construction involves the selection of a pair of strictly comparable weighted means (M, N), typically instantiated as quasi-arithmetic or power means: $$I_\alpha^{M,N}[p:q] = \frac{1}{\alpha(1-\alpha)} \int \left( M_{1-\alpha}(p, q) - N_{1-\alpha}(p, q) \right) d\mu$$ where the means may themselves be specified either by explicit formulas or as induced by continuous, strictly monotone generating functions (cf. quasi-arithmetic means $M_a^f(x, y) = f^{-1}((1-a)f(x) + a f(y))$).

When $M$ and $N$ are chosen as the weighted arithmetic mean (A) and geometric mean (G), this reproduces the traditional α-divergence. By varying the means (including power, harmonic, or Lehmer means) or by forming vector or composite means, one “combs” through a structured family of divergence measures, recovering and extending classical cases as well as forming new divergences with tailored properties (Nielsen, 2020, Nielsen et al., 2017, Roy et al., 7 Jul 2025).

Generalized comb divergences can also be constructed as explicit functional aggregates—using vector-indexed or multi-parameter combinations: $D[p:q] = \sum_{i=1}^K w_i D_i(p, q)$ where each $D_i$ can itself be an instance of a divergence from some parameterized family (e.g., different α or β in α- or β-divergence families), with the weights and indices set to emphasize particular features of interest. The “comb” structure is thus not only parametric (allowing smooth interpolation between types), but also combinatorial (permitting aggregation).

2. Key Analytical and Structural Properties

Generalised comb divergences, when formed via comparable means or suitable generating functions, satisfy axiomatic divergence criteria: non-negativity, separation (i.e., identity of indiscernibles), convexity in arguments, as well as duality or symmetry under parameter/argument inversion (Roy et al., 7 Jul 2025, Nielsen, 2020). For instance, in the quasi-arithmetic mean construction, strict comparability (i.e., for all $x, y$ and $a \in (0,1)$, $M_a(x, y) \leq N_a(x, y)$ with equality iff $x = y$) is a necessary and sufficient condition to guarantee divergence properties (Nielsen, 2020). In the context of the generalized alpha-beta (GAB) divergence framework, the convexity and monotonicity of the generating function ψ or its logarithmic transform $\Psi(x) = \psi(e^x)$ are necessary and sufficient for non-negativity and neutrality of the divergence (Roy et al., 7 Jul 2025).

Advanced structural properties include:

• Homogeneity (bipower or scaling properties, e.g., for homogeneous α-divergences): $$I_\alpha^{(r,s)}[\lambda p : \lambda q] = \lambda I_\alpha^{(r,s)}[p : q]$$.
• Semi-continuity and, in regular cases, continuity with respect to arguments.
• Pythagorean relations (approximate additivity under convex combinations), which closely relate to variational projection in robust statistics and information geometry.

Importantly, for square-rooted forms of certain generalized symmetric comb divergences, the metric property (non-negativity, symmetry, triangle inequality, and identity of indiscernibles) has been rigorously established (Costa et al., 2011).

3. Explicit Decompositions and Dual Representations

Generalised comb divergences often decompose naturally into interpretable components—particularly cross-entropy and entropy terms, or as (possibly conformal) Bregman divergences. For example, for quasi-arithmetic means generated by f and g, the generalized 1-divergence has the decomposition: $$I_1^{(f,g)}[p:q] = \int \left( \frac{f(q) - f(p)}{f'(p)} - \frac{g(q) - g(p)}{g'(p)} \right) d\mu$$ or, equivalently, as the difference between a generalized cross-entropy and a generalized entropy: $I_1^{(f,g)}[p:q] = h_x^{(f,g)}(p:q) - h^{(f,g)}(p)$ with

$$h_x^{(f,g)}(p:q) = \int \frac{f(q)}{f'(p)} - \frac{g(q)}{g'(p)} d\mu, \quad h^{(f,g)}(p) = \int \frac{f(p)}{f'(p)} - \frac{g(p)}{g'(p)} d\mu.$$

This dual representation enables explicit algebraic manipulation and links with differential geometric interpretations; the divergence can be rewritten as a conformal Bregman divergence using an embedding $F = f \circ g^{-1}$: $$I_1^{(f,g)}[p:q] = \int \frac{1}{f'(p)} B_F(g(q) : g(p)) d\mu$$ where $B_F$ is the standard Bregman divergence. This reveals underlying structures (e.g., dual flatness) and shows how comb divergences generalize familiar objects in information geometry (Nielsen, 2020).

4. Parametric Families and Multivariate Extensions

Parametric flexibility is a haLLMark of generalised comb divergences. The α-divergence, β-divergence, α-β-divergence, and their quasi-arithmetic and extended-parametric counterparts can be chosen to interpolate between different classical cases (e.g., KL, reverse KL, Hellinger, χ², Rényi/Tsallis) or to form one-parameter/two-parameter superfamilies (Roy et al., 7 Jul 2025, Yilmaz, 2013).

The generalized alpha-beta (GAB) divergence is defined via a generating function ψ and parameters α, β, yielding

$d_{\text{GAB}}^{(α,β), ψ}(P, Q),$

with characterizing properties (symmetry, scaling, duality) and strict conditions for validity derived from the convexity of Ψ. It includes density power divergence, beta divergence, and logarithmic density power divergences as special instances. The associated generalized α-β-entropy (GABE) is given by

$$ε_{\text{GAB}}^{(α,β), ψ}(P) = - \frac{1}{β} \left( \frac{\psi(\{p\}_{α+β}^{α+β})}{α+β} - \frac{\psi(\{p\}_{α}^{α})}{α} \right).$$

Such parametric and structural generalizations enable adaptation to diverse statistical tasks with tailored robustness or efficiency (Roy et al., 7 Jul 2025).

Multivariate or vector-skew constructions—such as the vector-skew Jensen–Shannon divergence—further expand the flexibility of comb divergences. For instance: $$\text{JS}^{(α,w)}(p:q) = h((pq)_{\bar{α}}) - \sum_{i=1}^k w_i h((pq)_{α_i})$$ where $\bar{\alpha} = \sum_i w_i α_i$, and $h$ is the entropy functional. This “comb” of skew parameters allows the design of rich divergence families that interpolate symmetries and asymmetries (Nielsen, 2019).

5. Applications in Information Theory, Statistics, and Learning Theory

Generalised comb divergences have broad applications:

• Error Bounds and Hypothesis Testing: Tight lower bounds on α-divergences under moment constraints provide operational optimality guarantees in classification and discrimination tasks (Nishiyama, 2021). In the generalised Pinsker framework, improved lower bounds on f-divergences in terms of variational divergences and Bayes risk curves translate directly into sharper performance guarantees in statistical learning (0906.1244).
• Robust Bayesian Inference: In robust Bayesian inference, generalized belief updates formed via f-divergence losses—possibly estimated via classifier-based density ratio estimation—enable adaptation to models under misspecification, offering resilience to tail effects or outliers (Thomas et al., 2020).
• Clustering and Learning Geometry: The metric properties and explicit barycenter computation algorithms associated with generalized symmetric or Jensen-type divergences make them suitable for clustering, kernel methods, and manifold-based learning, where the divergence metric defines the geometry of the problem (Costa et al., 2011, Nielsen, 2017, Nielsen et al., 2017).
• Nonextensive Physics and Multifractal Analysis: The emergence of Tsallis-type extended divergence measures in the asymptotic expansion of q-deformed multinomial distributions underpins modeling in nonextensive statistical mechanics, with the “comb” of higher-order divergences capturing deviations from classical entropy production (Okamura, 22 Aug 2024).
• Maximum Entropy and Robust Estimation: The associated entropy measures (GABE and variants) derived from comb divergences can be used in generalized maximum entropy inference under moment or escort mean constraints, producing new classes of maximum entropy distributions tailored to robust modeling or nontraditional statistical regimes (Roy et al., 7 Jul 2025).

6. Connections to Classical Divergences and Further Directions

Generalised comb divergences encapsulate many classical divergences as special or limiting cases:

• The choice of means (arithmetic, geometric, harmonic) or generating functions directly recovers α-divergence, KL-divergence, Hellinger, χ², Jensen–Shannon, Jeffreys, and Rényi/Tsallis divergence.
• The broader framework, utilizing comparable means and embedding/statistical geometry considerations, systematically produces new divergences with explicit formulas, dualities, and scaling/homogeneity properties.
• Comb divergences facilitate multi-scale or multi-feature analysis, where aggregate divergence can be designed to account for hierarchical, heterogeneous, or structured data characteristics (Taneja, 2011, Furuichi et al., 2011, Nielsen, 2019).

Current directions include rigorous characterization of new divergence families (e.g., generalized alpha-beta divergence), analysis of their metric and convexity properties, exploration of their operational meaning in statistical learning, and applications to model uncertainty quantification, robust optimization, nonextensive systems, and beyond (Roy et al., 7 Jul 2025, Dupuis et al., 2019).

7. Summary Table: Key Generalised Comb Divergence Families

Family/Type	Defining Mechanism/Parameters	Notable Properties/Features
α-divergence (generalized)	Two comparable means (M, N), parameter α	Recovers KL, Hellinger, χ², reverse KL; strict comparability key (Nielsen, 2020)
GAB divergence	Generating function ψ, α, β	Symmetry, duality, scaling, broad superfamily; unified entropy (Roy et al., 7 Jul 2025)
Quasi-arithmetic divergence	Functions f, g (means), α	Explicit cross-entropy/entropy decomposition; conformal Bregman link
Vector/Jensen–Shannon	Skew param. vector α, weights w	Parametric comb of Jensen–Shannon measures, symmetrizability (Nielsen, 2019)
Extended Tsallis divergences	q-param, expansion parameter	Tunable divergence family, nonextensive/statistical mechanics (Okamura, 22 Aug 2024)

• (0906.1244) Generalised Pinsker Inequalities
• (Taneja, 2011) Generalized Symmetric Divergence Measures and the Probability of Error
• (Furuichi et al., 2011) Mathematical inequalities for some divergences
• (Costa et al., 2011) Generalized Symmetric Divergence Measures and Metric Spaces
• (Yilmaz, 2013) Generalized Beta Divergence
• (Nielsen et al., 2017) Generalizing Jensen and Bregman divergences with comparative convexity...
• (Nielsen, 2017) A generalization of the Jensen divergence: The chord gap divergence
• (Nishiyama, 2018) Generalized Bregman and Jensen divergences which include some f-divergences
• (Nielsen et al., 2019) A note on the quasiconvex Jensen divergences and the quasiconvex Bregman divergences derived thereof
• (Dupuis et al., 2019) Formulation and properties of a divergence used to compare probability measures without absolute continuity
• (Nielsen, 2019) On a generalization of the Jensen-Shannon divergence
• (Nielsen, 2020) The α-divergences associated with a pair of strictly comparable quasi-arithmetic means
• (Thomas et al., 2020) Generalised Bayes Updates with $f$-divergences through Probabilistic Classifiers
• (Nishiyama, 2021) Tight Lower Bounds for $α$-Divergences Under Moment Constraints...
• (Okamura, 22 Aug 2024) On the $q$-generalised multinomial/divergence correspondence
• (Roy et al., 7 Jul 2025) Characterization of Generalized Alpha-Beta Divergence and Associated Entropy Measures

Generalised comb divergences thus provide a principled, mathematically rigorous, and practically flexible framework for measuring statistical dissimilarity, supporting advanced inference and learning tasks across theory and applications.