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The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
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- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Markov's inequality for random variables
Statement
If is a nonnegative random variable on a probability space and , then
Facts & Assumptions
Given: A nonnegative random variable and a real number .
Expectation is integration against the probability measure (Expectation of a nonnegative or integrable random variable).
The integral Markov inequality states for nonnegative measurable and (Chebyshev-Markov inequality for the integral).
Proof
Apply [L2] to the probability measure , the function , and the threshold . Rewriting the integral by [L1] gives
Step 1.1 is exactly Markov's inequality for random variables.
Depends on
Used by
- Chebyshev's inequality for random variables Corollary
- Common drift and minorization give containment under diminishing adaptation, strong aperiodicity, and a finite initial V-moment Corollary
- Boundedness of first moments alone does not give uniform integrability Counterexample
- Diminishing adaptation and uniform ergodicity need not give a strong law Counterexample
- Pointwise resampling misses the long-run variance of an AR(1) mean Counterexample
- A finite identifiable model with posterior consistency Example
- Markov and Chebyshev sharpness Example
- Normal location fitted under the wrong variance Example
- Tightness from a uniform moment bound Example
- A bounded-density Gaussian bridge has a Borel multiscale law Lemma
- A one-sided DQM path yields an iid LAN proxy Lemma
- A variance-sensitive bound for bounded empirical suprema Lemma
- Adjacent iid vector functionals admit a finite-moment strong approximation Lemma
- Bernstein tail bound for independent bounded sums Lemma
- Bounded independent matrix Bernstein bound from the trace exponential Lemma
- Bounded independent vector rows admit a logarithmic cubed Gaussian coupling Lemma
- Bracketing entropy implies empirical-process asymptotic equicontinuity Lemma
- Common drift and strongly aperiodic minorization give a uniform geometric bound Lemma
- Compact-uniform LAN approximates compact-prior posteriors Lemma
- Conditional CLT for independent resampled weighted blocks Lemma
- Countably partitioned sieves yield posterior contraction by weighted entropy Lemma
- Exact block budget for the covariance geometric-median pilot Lemma
- Exponential averaged spectral projectors for a finite cq ensemble Lemma
- Exponential tests and KL mass force posterior exclusion Lemma
- Finite Dirichlet gamma representation and weighted Bernstein bound Lemma
- Finite moments control increments over short moving windows Lemma
- Finite-horizon SMC empirical laws converge for integrable tests Lemma
- Heterogeneous bounded atom vector rows admit a logarithmic cubed Gaussian coupling Lemma
- Independent Brownian motion straightens a Poisson clock Lemma
- Linear and quadratic Gaussian statistics have exponential tails Lemma
- Normalized product likelihood expansion under QMD Lemma
- Squared-norm concentration for real and normalized complex Gaussians Lemma
- Strong Brownian approximation implies overlapping-batch consistency Lemma
- Tests and a sieve tail imply posterior contraction from a denominator bound Lemma
- The posterior excludes large scales and mixing mass far from compact data Lemma
- Type-S posterior contracts in quotient log-density coordinates without a tail restriction on a log-Lipschitz prior Lemma
- Uniform finite-horizon moments for tamed Euler approximations Lemma
- Weighted overlapping-block moment and covariance limits Lemma
- Correct-scale slab bound for multivariate trimming Proposition
- The general inequalities compare cleanly with the published finite ones Remark
…and 31 more results.
Dependency tree · two levels
10 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Rick Durrett, Probability: Theory and Examples, 5th ed., Section 1.6.1 (standard reference, not scraped)
- J. R. Norris, Probability and Measure, Section 4.2 (standard reference, not scraped)