Rates of convergence of stochastically monotone and continuous time Markov models

74 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Renewal theory and computable convergence rates for geometrically ergodic Markov chains (2005, methodology): “Here we concentrate on the caseε p, which was studied by Roberts and Tweedie [15] and Fort [4].”
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm (2015, background): “The bounds in Theorem 9 and Theorem 10 depend upon the constants appearing in Proposition 7 which are computable but are known to be pessimistic in general; see [36].”
SUBGEOMETRIC ERGODICITY OF STRONG MARKOV PROCESSES (2005, background): “Meyn and Tweedie [23] prove that the condition AV ≤−f outside a closed petite set is related (a) to the f -ergodicity of the Markov process X ; and (b) to the geometric V -ergodicity if f ∝ V (see also [5, 26, 29]).”
QUANTITATIVE CONVERGENCE RATES OF MARKOV CHAINS: A SIMPLE ACCOUNT (2002, background): “The minorisation condition (1) can be relaxed to a pseudo-minorisation condition, where the measure ν = νx,x′ may depend upon the pair (x, x′) ∈ C × C (Roberts and Rosenthal, 2000).”