Sequential Quasi-Monte Carlo

We derive and study SQMC (Sequential Quasi-Monte Carlo), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer et al. (2006). The complexity of SQMC is $O(N \log N)$, where $N$ is the number of simulations at each iteration, and its error rate is smaller than the Monte Carlo rate $O_P(N^{-1/2})$. The only requirement to implement SQMC is the ability to write the simulation of particle $x_t^n$ given $x_{t-1}^n$ as a deterministic function of $x_{t-1}^n$ and a fixed number of uniform variates. We show that SQMC is amenable to the same extensions as standard SMC, such as forward smoothing, backward smoothing, unbiased likelihood evaluation, and so on. In particular, SQMC may replace SMC within a PMCMC (particle Markov chain Monte Carlo) algorithm. We establish several convergence results. We provide numerical evidence that SQMC may significantly outperform SMC in practical scenarios.Comment: 55 pages, 10 figures (final version

Plug-and-play inference for disease dynamics: measles in large and small populations as a case study

Statistical inference for mechanistic models of partially observed dynamic systems is an active area of research. Most existing inference methods place substantial restrictions upon the form of models that can be fitted and hence upon the nature of the scientific hypotheses that can be entertained and the data that can be used to evaluate them. In contrast, the so-called plug-and-play methods require only simulations from a model and are thus free of such restrictions. We show the utility of the plug-and-play approach in the context of an investigation of measles transmission dynamics. Our novel methodology enables us to ask and answer questions that previous analyses have been unable to address. Specifically, we demonstrate that plug-and-play methods permit the development of a modelling and inference framework applicable to data from both large and small populations. We thereby obtain novel insights into the nature of heterogeneity in mixing and comment on the importance of including extra-demographic stochasticity as a means of dealing with environmental stochasticity and model misspecification. Our approach is readily applicable to many other epidemiological and ecological systems

A New Cell Bypass Arrangement and Control for Modular Multilevel Converters based on Thyristor Forced Commutation Circuit

HVDC transmission lines are a competitive and in some cases are proven to bea superior choice compared to AC transmission applications. Suitable convertershave been developed for that matter where Modulär Multilevel Converters (MMC)are highly preferred due to their low losses, no filtering requirementsand direct andfast control of AC and DC side. However, the overall eciency of the converteris higher than of a six pulse voltage source converter, it is still lower than the linecommutated converter type.In this master thesis an attempt to decrease the conduction losses of the MMC isinvestigated. A new cell structure design used in MMC is proposed along with itsassociated control strategy. The main idea is to divert the current at steady stateoperation through thyristors, which have lower conduction resistance than IGBTsthat are used in MMC topologies, at time intervals where the capacitor is bypassedfrom the cell. This new cell commutation is tested initially in the lab and thenthe whole structure operation is validated on a 3 phase MMC PSCAD model. Theresults from the lab confirmed the commutation of the new cell and the results fromthe 3 phase model showed that the new cell structure does not disturb the normaloperation of the MMC. A rough loss comparison that have been conducted betweenthe new cell structure and a half bridge that is used in a typical MMC, showed thatthe first one was less efficient. For that reason a generalized concept is introducedwhich promise higher efficiency than of the proposed concept.HVDC transmission är ett fördelaktigt sätt att överföra eekt i jämförelse med ACtransmission. Omriktare har utvecklats för att passa applikationen, där ModularMultilevel Converters (MMC) har visat sig passa bra för HVDC på grund av de lågaförlusterna och dess obentliga krav på lter. Dessutom har de en direkt och snabbkontrollteori på både AC och DC sidan. Även om dess totala verkningsgrad är högreän hos six-pulse voltage source converter (VSC) men lägre än Line CommutatedConverter (LCC).Detta exjobb innefattar att minska ledningsförlusterna i MMCn. En ny designav cell strukturen föreslås, tillsammans med en passande kontrollteori. Idén äratt, på grund av dess lägre ledningsresistans använda tyristorer snarare än IGBTervilka annars är vanliga i MMCer, detta då kondensatorn är förbikopplad.. Dennya cellstrukturen testas initialt experimentellt i laboratorium och hela systemetvalideras genom simulering av en 3-fas MMC modell i PSCAD. De experimentellaresultaten bekräftade att den nya modellen fungerar och de simulerade resultatenvisar att den föreslagna topologin inte stör funktionen hos MMCn. En jämförelsemellan den nya topologin och den konventionella halvbridge strukturen har gjorts,där den föreslagna topologin hade lägre verkningsgrad. Istället har en generelltkoncept introducerats för att utlova en högre verkningsgrad än den först föreslagnatopologin

Online parameter estimation for partially observed diffusions

This paper proposes novel particle methods for online parameter estimation for partially observed diffusions. We consider diffusions observed with error under a non-linear mapping and multivariate diffusions where only a subset of the components is observed. The proposed methods rely on the commonly used idea of data augmentation and are based on obtaining particle approximations to the derivatives of the optimal filter. The performance of our algorithms is assessed using several financial applications. © 2006 IEEE

Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models

In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE) of the static parameters of a general state space model. Our approach is based on viewing the particle filter as a controlled Markov chain, where the control is the unknown static parameters to be identified, The algorithm relies on the computation of the gradient of the particle filter using a score function approach. © 2006 IEEE

Particle filter as a controlled Markov chain for on-line parameter estimation in general state space models

In this paper we present a novel optimization method for on-line maximum likelihood estimation (MLE) of the static parameters of a general state space model. Our approach is based on viewing the particle filter as a controlled Markov chain, where the control is the unknown static parameters to be identified, The algorithm relies on the computation of the gradient of the particle filter using a score function approach. © 2006 IEEE

Gradient-free maximum likelihood parameter estimation with particle filters

In this paper we address the problem of on-line estimation of unknown static parameters in non-linear non-Gaussian state-space models. We consider a particle filtering method and employ two gradient-free Stochastic Approximation (SA) methods to maximize recursively the likelihood function, the Finite Difference SA and Spall's Simultaneous Perturbation SA. We demonstrate how these algorithms can generate maximum likelihood estimates in a simple and computationally efficient manner. The performance of the proposed algorithms is assessed through simulation. © 2006 IEEE