Peter Baxendale

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We give computable bounds on the rate of convergence of the transition probabilities to the stationary distribution for a certain class of geometrically ergodic Markov chains. Our results are different from earlier estimates of Meyn and Tweedie, and from estimates using coupling, although we start from essentially the same assumptions of a drift condition(More)
The stability problem of the Wonham filter with respect to initial conditions is addressed. The case of ergodic signals is revisited in view of a gap in the classic work of H. Kunita (1971). We give new bounds for the exponential stability rates, which do not depend on the observations. In the nonergodic case, the stability is implied by identifiability(More)
Simulations of models of epidemics, biochemical systems, and other bio-systems show that when deterministic models yield damped oscillations, stochastic counterparts show sustained oscillations at an amplitude well above the expected noise level. A characterization of damped oscillations in terms of the local linear structure of the associated dynamics is(More)
Consider the stochastic Duffing-van der Pol equation ẍ = −ω2x− Ax −Bx2ẋ + εβẋ + εσxẆt with A ≥ 0 and B > 0. If β/2 + σ/8ω > 0 then for small enough ε > 0 the system (x, ẋ) is positive recurrent in R \ {0}. Let λ̃ε denote the top Lyapunov exponent for the linearization of this equation along trajectories. The main result asserts that λ̃ε ∼ ελ̃ as ε → 0 where(More)
Let λ denote the almost sure Lyapunov exponent obtained by linearizing the stochastic Duffing-van der Pol oscillator ẍ = −ωx + βẋ−Ax −Bxẋ + σxẆt at the origin x = ẋ = 0 in phase space. If λ > 0 then the process {(xt, ẋt) : t ≥ 0} is positive recurrent on R \ {(0, 0)} with stationary probability measure μ, say. For λ > 0 let λ̃ denote the almost sure(More)
Integrate and fire oscillators are widely used to model the generation of action potentials in neurons. In this paper, we discuss small noise asymptotic results for a class of stochastic integrate and fire oscillators (SIFs) in which the buildup of membrane potential in the neuron is governed by a Gaussian diffusion process. To analyze this model, we study(More)
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