state space
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2022 ◽  
Vol 217 ◽  
pp. 105270
Wildon Panziera ◽  
Claudia Liane Rodrigues de Lima ◽  
Luís Carlos Timm ◽  
Leandro Sanzi Aquino ◽  
Willian Silva Barros ◽  

2022 ◽  
Vol 166 ◽  
pp. 108448
David Vališ ◽  
Jakub Gajewski ◽  
Marie Forbelská ◽  
Zdeněk Vintr ◽  
Józef Jonak

2022 ◽  
Vol 253 ◽  
pp. 113788
Xingxi Liu ◽  
Yun Wang ◽  
Guannan Wang ◽  
Bo Yang ◽  
Rongqiao Xu

2022 ◽  
pp. 1471082X2110657
Sina Mews ◽  
Roland Langrock ◽  
Marius Ötting ◽  
Houda Yaqine ◽  
Jost Reinecke

Continuous-time state-space models (SSMs) are flexible tools for analysing irregularly sampled sequential observations that are driven by an underlying state process. Corresponding applications typically involve restrictive assumptions concerning linearity and Gaussianity to facilitate inference on the model parameters via the Kalman filter. In this contribution, we provide a general continuous-time SSM framework, allowing both the observation and the state process to be non-linear and non-Gaussian. Statistical inference is carried out by maximum approximate likelihood estimation, where multiple numerical integration within the likelihood evaluation is performed via a fine discretization of the state process. The corresponding reframing of the SSM as a continuous-time hidden Markov model, with structured state transitions, enables us to apply the associated efficient algorithms for parameter estimation and state decoding. We illustrate the modelling approach in a case study using data from a longitudinal study on delinquent behaviour of adolescents in Germany, revealing temporal persistence in the deviation of an individual's delinquency level from the population mean.

2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-27
Xuan-Bach Le ◽  
Shang-Wei Lin ◽  
Jun Sun ◽  
David Sanan

It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various quantum programs including Deutsch–Jozsa’s algorithm and Grover's algorithm.

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 251
Virginia Giorno ◽  
Amelia G. Nobile

We consider a time-inhomogeneous Markov chain with a finite state-space which models a system in which failures and repairs can occur at random time instants. The system starts from any state j (operating, F, R). Due to a failure, a transition from an operating state to F occurs after which a repair is required, so that a transition leads to the state R. Subsequently, there is a restore phase, after which the system restarts from one of the operating states. In particular, we assume that the intensity functions of failures, repairs and restores are proportional and that the birth-death process that models the system is a time-inhomogeneous Prendiville process.

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