Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion

Dabrowski, Joel Janek; Villiers, J. P. de; Beyers, Conrad

doi:10.1016/j.inffus.2017.10.002

Cited by 16 publications

(16 citation statements)

References 33 publications

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“…3. The dynamic Bayesian network structure of the naïve Bayes switching linear dynamic system (NB-SLDS) for three input variables ( Dabrowski et al, 2016 ). Each input variable is associated with a separate LDS.…”

Section: Learningmentioning

confidence: 99%

“…The naïve Bayes switching linear dynamic system (NB-SLDS) is a variant of the SLDS ( Dabrowski, de Villiers, & Beyers, 2016 ). The graphical model of the NB-SLDS is presented in Fig.…”

Section: Naïve Bayes Switching Linear Dynamic Systemmentioning

confidence: 99%

“…Adding variables to the NB-SLDS results in a linear increase in the dimensionality of the system. For a set of N variables, the state equations for the n th LDS in the NB-SLDS are given by (Dabrowski et al, 2016) …”

Section: Naïve Bayes Switching Linear Dynamic Systemmentioning

confidence: 99%

“…In the NB-SLDS, however, there are N LDSs that are processed independently. The derivations for the inference algorithms are presented in Dabrowski et al (2016) . The GSF method is applied for filtering and the GPB algorithm is applied for smoothing.…”

Section: Inferencementioning

confidence: 99%

“…The propagation for all S switching states produces an S × S component Gaussian mixture. Each component in the mixture is parametrised by the following mean and covariance ( Dabrowski et al, 2016 ) …”

Section: Filteringmentioning

confidence: 99%

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Systemic banking crisis early warning systems using dynamic Bayesian networks

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2016

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Section: Learningmentioning

confidence: 99%

“…The naïve Bayes switching linear dynamic system (NB-SLDS) is a variant of the SLDS ( Dabrowski, de Villiers, & Beyers, 2016 ). The graphical model of the NB-SLDS is presented in Fig.…”

Section: Naïve Bayes Switching Linear Dynamic Systemmentioning

confidence: 99%

Section: Naïve Bayes Switching Linear Dynamic Systemmentioning

confidence: 99%

Section: Inferencementioning

confidence: 99%

Section: Filteringmentioning

confidence: 99%

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Systemic banking crisis early warning systems using dynamic Bayesian networks

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Beyers

Villiers

2016

Expert Systems with Applications

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Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

Dabrowski¹,

Villiers²,

Rahman³

et al. 2019

Communications in Computer and Information Science

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The time it takes for a classifier to make an accurate prediction can be crucial in many behaviour recognition problems. For example, an autonomous vehicle should detect hazardous pedestrian behaviour early enough for it to take appropriate measures. In this context, we compare the switching linear dynamical system (SLDS) and a three-layered bi-directional long short-term memory (LSTM) neural network, which are applied to infer pedestrian behaviour from motion tracks. We show that, though the neural network model achieves an accuracy of 80%, it requires long sequences to achieve this (100 samples or more). The SLDS, has a lower accuracy of 74%, but it achieves this result with short sequences (10 samples). To our knowledge, such a comparison on sequence length has not been considered in the literature before. The results provide a key intuition of the suitability of the models in time-critical problems.

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Naïve Bayes switching linear dynamical system: A model for dynamic system modelling, classification, and information fusion

Cited by 16 publications

References 33 publications

Systemic banking crisis early warning systems using dynamic Bayesian networks

Systemic banking crisis early warning systems using dynamic Bayesian networks

Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction

A Method for Processing Elevator Brake Performance Degradation Data Using Multi-source Sensor Data Fusion and Feature Tracking

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