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Hamiltonian Monte Carlo and leapfrog correction — Examples
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Areas of Elementary Plane Figures
- Binary Operations, Monoids, Groups and Subgroups
- Central Limit Theorems
- Characteristic Functions Inversion and Continuity
- Codimension One Foliations, Secondary Classes and Characteristic Disk Foundations
- Compactness
- Compactness in Metric Spaces
- Complete Metrizability, Čech-Completeness, and Baire Category
- Completeness, Completion, and Uniform Continuity
- Complex Lp Spaces and Test-Function Conventions
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Computational foundations: factorization, KL, and optimization
- Conditional Distributions and Regular Conditional Probability
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- 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
- Cyclic Groups and Direct Products
- Density Separability and Convolution in Lᵖ
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Direct Matrix Factorisations: LU, Cholesky and QR
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Domination and Likelihood Ratios
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Probability and the Probabilistic Method
- Foundations of the Real Numbers for Analysis
- Fourier Transform Convolution and Approximate Identities
- Fubini and Change of Variables
- Fundamental Trigonometric Identities
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hamiltonian Monte Carlo and leapfrog correction
- Hausdorff via the Diagonal
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Improper and Parameter-Dependent Multiple Integrals
- Improper Integrals
- Independence Borel Cantelli and Zero One Laws
- Infinite Product Measures and Kolmogorov Extension
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Lebesgue Measure on Euclidean Space
- 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
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Matrix Differentiation and First-order Spectral Perturbation
- Matrix Norms, Condition Numbers and Numerical Stability
- Measurable Functions and Simple Approximation
- Measures and Their Basic Properties
- Metric Spaces
- Metropolis–Hastings kernels
- Mixed Partials, Taylor Formulae, and Extrema
- Modes of Convergence Egorov and Lusin
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Outer Measure and the Caratheodory Extension Theorem
- Partitions of Unity and Paracompactness
- Picard-Lindelöf and First-Order Ordinary Differential Equations
- Pointwise Extended-Real Function Products
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Probability Spaces Random Variables and Expectation
- Product Measures and the Fubini Tonelli Theorems
- Properties of the Integral and the Working FTC
- Radon Measures and the Riesz Markov Kakutani Theorem
- 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
- Sine, Cosine, and the Definition of Pi
- Splitting Fields
- Statistical Experiments, Models and Identifiability
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Cantor Set, Baire Category, and Measure Zero in ℝ
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Inverse and Implicit Function Theorems
- The Lebesgue and Riemann Integrals Compared
- The Lebesgue Integral and the Convergence Theorems
- The Logarithm and General Powers
- The Lᵖ Spaces Holder Minkowski and Riesz Fischer
- The Maximal Function and Lebesgue Differentiation
- The Radon Nikodym Theorem and Lebesgue Decomposition
- The Riemann Integral in Rᵐ and Jordan Content
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Topology of Euclidean Space
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Urysohn's Lemma and the Tietze Extension Theorem
- Vector Spaces, Linear Subspaces, Span and Direct Sums
- Weak Convergence Tightness and Representation
2 · Summary
The examples verify a globally Lipschitz force and its exact-flow stability bound, an exactly solvable Gaussian flow, a harmonic leapfrog resonance that leaves position unchanged, and a finite-step energy error with its Metropolis acceptance probability. They separate exact target invariance from energy conservation and exploration: the corrected kernel can preserve the target while a resonant trajectory remains trapped at a point.
3 · Logical flowchart
4 · Definitions, theorems and proofs
None yet.
5 · Examples, counterexamples and false statements
A globally Lipschitz Hamiltonian force
Example
Take , , and . This gives a proper target, a globally -Lipschitz force, and a complete exact Hamiltonian flow with the Gronwall stability estimate from A complete Hamiltonian flow for a C2 potential preserves energy and phase volume.
Facts & Assumptions
Given: AC, , unit mass, and the one-dimensional Euclidean metric.
The HMC target is and its Hamiltonian vector field is . Hamiltonian Monte Carlo
A globally state-Lipschitz ODE vector field has a unique global solution. A globally state-Lipschitz vector field on has global solutions
Solutions of a state-Lipschitz ODE obey the Gronwall stability estimate. The Grönwall estimate for two solutions of a Lipschitz ODE
Under Countable Choice, a complete Hamiltonian flow preserves and phase volume; a globally Lipschitz gradient gives the stated sufficient completeness and stability bound. A complete Hamiltonian flow for a C2 potential preserves energy and phase volume
If a function is continuous on a closed interval and differentiable on its interior, its derivative at some interior point equals the average rate of change. The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with
AC implies Countable Choice, the axiom required for the exact-flow volume result. AC supplies countable selections and prescribed serial paths
Under Countable Choice, the function is nonnegative, measurable, and Lebesgue integrable. The real-line exponential tail is Lebesgue integrable
The exponential is positive and strictly increasing; under Countable Choice, the measure of is its length . The exponential is positive and satisfies , The exponential function is strictly increasing, A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included
For nonnegative measurable functions, the nonnegative integral is monotone under pointwise domination. Monotonicity and nonnegative homogeneity of the nonnegative integral
On a measure space, for nonnegative measurable and measurable , . If on , monotonicity and the simple-function definition give . Integral over a measurable subset, Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function
Proof
For , , so monotonicity of the exponential gives ; [F8]–[F10] imply For all , , hence ; by [F6] AC supplies the Countable Choice required by [F7], and [F9] gives . Differentiating gives Applying [F5] to , which is continuous on each closed interval and differentiable on its interior, the bound makes globally -Lipschitz.
For and , Thus the Hamiltonian vector field is globally -Lipschitz.
Apply [F2] to obtain the unique global flow. Since , [F4] and its Countable Choice hypothesis supplied by [F6] give energy and volume preservation; its sufficient-condition bound has .
Applying [F3] with the same constant gives for every pair of initial states and every real . This is a flow-stability bound, not a discretization-error estimate.
The exact-flow Hamiltonian Monte Carlo limit
Example
For the standard-normal target with , exact integration for time , followed by the required momentum flip, proposes an independent standard-normal position and has acceptance probability one. This is the exact-flow limit; a finite leapfrog map generally has a nonzero energy error.
Facts & Assumptions
Given: AC, , , current position , refreshed momentum , and exact integration time .
The HMC target is proportional to , the kinetic energy is , and the momentum is refreshed independently from . Hamiltonian Monte Carlo
The standard normal law has density . Standard normal and normal laws
Under Countable Choice, , and completeness, the exact Hamiltonian flow preserves and phase volume. A complete Hamiltonian flow for a C2 potential preserves energy and phase volume
A globally state-Lipschitz vector field has a unique global solution through each initial state. A globally state-Lipschitz vector field on has global solutions
AC implies Countable Choice, the axiom required for the exact-flow volume result. AC supplies countable selections and prescribed serial paths
Proof
Here Hamilton's equations are and . For and , , so the field is globally -Lipschitz. The curves satisfy these equations and the initial condition . By [F4], this global solution is the unique exact flow through ; [F3] applies with the Countable Choice hypothesis supplied by [F5].
At , the exact endpoint is ; flipping momentum gives . Since , and the Metropolis acceptance probability is .
The returned position is the refreshed , which is independent of the old and has law by [F2]. Thus every update returns an independent draw from the target. This exact-flow argument does not apply to a finite leapfrog discretization.
A Gaussian harmonic leapfrog resonance
Example
For , , , and , the leapfrog trajectory returns exactly to its initial phase point before the momentum flip. The accepted position therefore never moves; starting at is a trapped state although the standard-normal target assigns positive probability to .
Facts & Assumptions
Given: AC, the one-dimensional HMC target , unit mass, step size , and leapfrog steps.
The one-step map is the composition of a half force kick, full drift, and half force kick. One-step leapfrog map as force and drift shears
The HMC proposal flips momentum and accepts according to the Hamiltonian difference. Leapfrog Hamiltonian Monte Carlo
Under AC, the standard-normal density is . Standard normal and normal laws
The HMC proposal is the momentum flip composed with the -step leapfrog map, and its correction uses the Hamiltonian difference. Leapfrog Hamiltonian Monte Carlo
AC implies Countable Choice, the assumption needed for the Lebesgue box measure used below. AC supplies countable selections and prescribed serial paths
The exponential is positive and strictly increasing, and under Countable Choice . The exponential is positive and satisfies , The exponential function is strictly increasing, A box in with parameters is Lebesgue measurable of measure , whichever of its faces are included
The probability of a Borel set is the integral of its density; the nonnegative integral is monotone, and a constant integrates over to . Integral over a measurable subset, Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function
Proof
For and , the half kick and drift matrices are Multiplication in the order from [F1] gives
Substituting gives and direct multiplication yields , hence . Thus after four steps . By [F4] the flipped proposal is , whose Hamiltonian equals the initial Hamiltonian because is even.
By [F2], acceptance is one and every returned position equals the input . Starting at therefore gives law at every iteration. On , the density [F3] is at least , so [F6] and [F7] give target mass at least . Thus the chain remains trapped at a point while the target is not a point mass.
Finite-step leapfrog energy error
Example
For the one-dimensional standard-normal potential , unit mass, , one leapfrog step, and initial phase point , the proposed energy rises by and the Metropolis acceptance probability is .
Facts & Assumptions
Given: AC, , , , and .
A leapfrog step applies a half kick, full drift, then half kick; the proposal flips the final momentum. One-step leapfrog map as force and drift shears The HMC correction uses . Hamiltonian Monte Carlo
The algorithm retains the original position if the proposal is rejected. Leapfrog Hamiltonian Monte Carlo
The exponential is positive and strictly increasing, and for every real ; at , positivity and the reciprocal identity give , hence . The exponential is positive and satisfies , The exponential function is strictly increasing
Proof
The first half kick gives . The drift gives . The final half kick gives , so the flipped proposal is .
The initial energy is , while the proposed energy is . The discrete step therefore does not conserve at this point.
The acceptance probability is , since and [F3] gives ; rejection retains by [F2]. This finite calculation distinguishes energy error from the exact target-invariance statement proved for the full Metropolized kernel.