Implicit particle methods and their connection with variational data assimilation

The implicit particle filter is a sequential Monte Carlo method for data assimilation that guides the particles to the high-probability regions via a sequence of steps that includes minimizations. We present a new and more general derivation of this approach and extend the method to particle smoothing as well as to data assimilation for perfect models. We show that the minimizations required by implicit particle methods are similar to the ones one encounters in variational data assimilation and explore the connection of implicit particle methods with variational data assimilation. In particular, we argue that existing variational codes can be converted into implicit particle methods at a low cost, often yielding better estimates, that are also equipped with quantitative measures of the uncertainty. A detailed example is presented

Distributed Bayesian Decision-Making: Further Experiments

Decentralized adaptive control is based on the use of many local controllers in parallel, each of them estimating its own local model and pursuing local aims. When each controller designs its strategy using only its model, the resulting control will be suboptimal since its local model is not predicting consequences of actions of the neighbors. We propose to improve this by exchange multistep predictors on common data between the neighboring controllers and their subsequent use in the design of control strategy. Care is taken to assure that the resulting design procedure is of the same complexity as the one without the exchange. Performance of the approach is illustrated on a simple example.

Variational Bayes for Orthogonal Probablistic

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Inference in Hybrid Bayesian Networks

Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees and reliability block diagrams). However, limitations in the BNs ’ calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade’s research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability