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- Hans R. Künsch
- 2003

Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept–reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and strati-fication can be used in the… (More)

Importance splitting is a simulation technique to estimate very small entrance probabilities for Markov processes by splitting sample paths at various stages before reaching the set of interest. This can be done in many ways, yielding different variants of the method. In this context, we propose a new one, called fixed number of successes. We prove… (More)

- Lorenzo Tomassini, Peter Reichert, Hans R. Künsch, Christoph Buser, Reto Knutti, Mark E. Borsuk +1 other
- 2006

Even after careful calibration, the output of deterministic models of environmental systems usually still show systematic deviations from measured data. To analyse possible causes of these discrepancies, we make selected model parameters time variable by treating them as continuous time stochastic processes. This extends an approach that was proposed… (More)

A noninvertible function of a first order Markov process, or of a nearest-neighbor Markov random field, is called a hidden Markov model. Hidden Markov models are generally not Markovian. In fact, they may have complex and long range interactions, which is largely the reason for their utility. Applications include signal and image processing, speech… (More)

— The generalization of the sampling theorem to multidimensional signals is considered, with or without bandwidth constraints. The signal is modeled as a stationary random process and sampled on a lattice. Exact expressions for the mean square error of the best linear interpo-lator are given in the frequency domain. Moreover, asymp-totic expansions are… (More)

- Markus Hürzeler, Hans R. Künsch, Aravind Sundaresan

1 Introduction • Consider a general State Space model, where – State transition: p(x t | x t−1) = f (x t−1 , τ) – Observation density: p(y t | x t) = f (x t , η) • We wish to estimate the parameters, θ = (τ T , η T) T. • We can do the following. – Bayesian Method: This approach is often used with the State-Space Models and obtains the posterior probability… (More)

We present a simulation methodology for Bayesian estimation of rate parameters in Markov jump processes arising for example in stochastic kinetic models. To handle the problem of missing components and measurement errors in observed data, we embed the Markov jump process into the framework of a general state space model. We do not use diffusion… (More)