A non-parametric Bayesian change-point method for recurrent events

Li, Qing; Guo, Feng; Kim, In-Young

doi:10.1080/00949655.2020.1792907

Cited by 8 publications

(6 citation statements)

References 45 publications

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“…In addition, models fitted by aggregated data can be insufficient to capture the non-homogeneous information. Following the work of Demiroluk and Ozbay, 2015 , Li et al, 2017 , Li et al, 2020 to model non-homogeneous crash intensity, we proposed to apply the GPMRP model, which is a modulated renewal stochastic process that relaxes the i.i.d. assumptions.…”

Section: Discussionmentioning

confidence: 99%

Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic

Lei¹,

Özbay²,

Xie³

2022

Accident Analysis & Prevention

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Section: Discussionmentioning

confidence: 99%

Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic

Lei¹,

Özbay²,

Xie³

2022

Accident Analysis & Prevention

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“…Arguably, deep learning is the state-of-the-art; however, production network traffic has no label, hence unsupervised learning is recommended [3][4][5]. Current trend on unsupervised learning over unstructured, especially in bio-technology and statistical computation, is on Bayesian model, specifically the non-parametric Bayesian model [6,7], thus it has motivated this research to explore a solution through Bayesian model.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Yusof¹,

Zin²,

Satar³

2022

Computers, Materials &Amp; Continua

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Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics, namely lack of prior information (p (θ)), and reduced parameters (θ). Therefore, this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction. Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion. Finally, the results are extended to evaluate detection, accuracy and false alarm rate of the model against the subject matter expert model, Support Vector Machine (SVM), k nearest neighbor (k-NN) using simulated and ground-truth dataset. The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario. Results have shown that the proposed model consistently outperformed other models.

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“…Lin et al (2017) also used parametric piecewise intensity functions. Li, Guo, and Kim (2020) considered nonparametric Bayesian framework in recurrent event applications. In this paper, the baseline intensity functions are left unspecified.…”

Section: Introductionmentioning

confidence: 99%

Copula-Frailty Models for Recurrent Event Data Based on Monte Carlo EM Algorithm

Bedair¹,

Hong²,

Al‐Khalidi³

2021

Preprint

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Multi-type recurrent events are often encountered in medical applications when two or more different event types could repeatedly occur over an observation period. For example, patients may experience recurrences of multi-type nonmelanoma skin cancers in a clinical trial for skin cancer prevention. The aims in those applications are to characterize features of the marginal processes, evaluate covariate effects, and quantify both the within-subject recurrence dependence and the dependence among different event types. We use copula-frailty models to analyze correlated recurrent events of different types. Parameter estimation and inference are carried out by using a Monte Carlo expectation-maximization (MCEM) algorithm, which can handle a relatively large (i.e., three or more) number of event types. Performances of the proposed methods are evaluated via extensive simulation studies. The developed methods are used to model the recurrences of skin cancer with different types.

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A non-parametric Bayesian change-point method for recurrent events

Cited by 8 publications

References 45 publications

Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic

Safety analytics at a granular level using a Gaussian process modulated renewal model: A case study of the COVID-19 pandemic

Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

Copula-Frailty Models for Recurrent Event Data Based on Monte Carlo EM Algorithm

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