Improving the Gibbs sampler

Park, Taeyoung; Lee, Seunghan

doi:10.1002/wics.1546

Cited by 7 publications

(5 citation statements)

References 33 publications

(41 reference statements)

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“…One key algorithm within the MCMC method is the Gibbs Sampling technique, as explained by [24] [25]. Gibbs Sampling simplifies complex calculations by generating random variables from the marginal distribution without the need for density calculations [26] [27]. It focuses on identifying the univariate conditional distribution, involving only one variable to be determined [28][29].…”

Section: The Markov Chain Monte Carlomentioning

confidence: 99%

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Rahayu

2023

J. Appl. Data Sci.

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The study aims to comprehensively analyze the spatial distribution and varying risk levels of Dengue Hemorrhagic Fever (DHF) within Aceh Province. The primary objective is to identify and delineate regions within Aceh Province that demonstrate diverse probabilities of DHF occurrences. By investigating the discrepancies in DHF cases and population susceptibility across districts and cities, the research intends to facilitate strategic planning and targeted interventions for disease mitigation and control. Utilizing secondary data sourced from the Aceh Provincial Health Profile spanning 2016 to 2022, this study employs the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). This statistiThe research significantly contributes to enhancing our understanding of DHF distribution patterns and associated risks within Aceh Province. By employing a robust statistical model and analyzing secondary health profile data, the study offers valuable insights into identifying areas with varying levels of DHF risk. The findings are pivotal in guiding evidence-based decision-making for targeted interventions, resource allocation, and strategic planning aimed at mitigating the impact of DHF in high-risk regions. The study's outcomes highlight notable fluctuations in mortality due to dengue cases within Aceh Province, particularly evident in the peaks observed during 2016 and 2022. Furthermore, through the Bayesian CAR (BYM) model, the research identifies districts and cities with varying relative risk values for DHF occurrences. Notably, Sabang city emerges with the highest relative risk value of 3.54, signifying elevated susceptibility, while Bener Meriah district demonstrates the lowest relative risk at 0.2, indicating lower vulnerability to DHF. These findings provide critical insights into the heterogeneous DHF risk landscape across Aceh Province, informing targeted interventions and planning strategies to effectively address the disease burden.

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Section: The Markov Chain Monte Carlomentioning

confidence: 99%

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Rahayu

2023

J. Appl. Data Sci.

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“…LDA is used to estimate the document topic distribution P(θ|d) and topic term distribution P(t|θ) using an unlabeled corpus of documents. Gibbs sampler [19] executes many times for each word ti in a document di and then samples a new subject j depending on them. Ctθ represents the number of topics, CDθ represents the number of documents containing topic assignments, T represents all subject assignments, θ-i represents all topic terms.…”

Section: Medium Level Event Detectionmentioning

confidence: 99%

Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

Mohammad,

Al-Ahmadi,

Al-Muhtadi

2023

Computers, Materials &Amp; Continua

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Social media forums have emerged as the most popular form of communication in the modern technology era, allowing people to discuss and express their opinions. This increases the amount of material being shared on social media sites. There is a wealth of information about the threat that may be found in such open data sources. The security of already-deployed software and systems relies heavily on the timely detection of newly-emerging threats to their safety that can be gleaned from such information. Despite the fact that several models for detecting cybersecurity events have been presented, it remains challenging to extract security events from the vast amounts of unstructured text present in public data sources. The majority of the currently available methods concentrate on detecting events that have a high number of dimensions. This is because the unstructured text in open data sources typically contains a large number of dimensions. However, to react to attacks quicker than they can be launched, security analysts and information technology operators need to be aware of critical security events as soon as possible, regardless of how often they are reported. This research provides a unique event detection method that can swiftly identify significant security events from open forums such as Twitter. The proposed work identified new threats and the revival of an attack or related event, independent of the volume of mentions relating to those events on Twitter. In this research work, deep learning has been used to extract predictive features from open-source text. The proposed model is composed of data collection, data transformation, feature extraction using deep learning, Latent Dirichlet Allocation (LDA) based medium-level cyber-event detection and final Google Trends-based high-level cyber-event detection. The proposed technique has been evaluated on numerous datasets. Experiment results show that the proposed method outperforms existing methods in detecting cyber events by giving 95.96% accuracy.

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“…This can be mostly done by constructing a Gibbs sampling algorithm with a blocking strategy into the consideration [303]. A general rule is that the convergence of the Gibbs sampler can be improved by grouping correlated latent variables as a single parameter block to sample from as a whole [304]. On the other hand, in the task of sampling from a non-closed form distribution, we know that a naive MH algorithm [180] requires the specification of proposal density, which can be problematic in developing software.…”

Section: Software Developmentmentioning

confidence: 99%

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

Lee

2022

Mathematics

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Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models has a long history, Bayesian analysis of the models has received comparatively little attention until the late 1980s, primarily due to the time-consuming nature of Bayesian computation. Since the early 1990s, Bayesian approaches for the models began to emerge to leverage rapid developments in computing power, and have recently received significant attention due to (1) superiority to quantify the uncertainty of parameter estimation; (2) utility to incorporate prior knowledge into the models; and (3) flexibility to match exactly the increasing complexity of scientific research arising from diverse industrial and academic fields. This review article presents an overview of modeling strategies to implement Bayesian approaches for the nonlinear mixed effects models, ranging from designing a scientific question out of real-life problems to practical computations.

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Improving the Gibbs sampler

Cited by 7 publications

References 33 publications

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method

Deep Learning Based Cyber Event Detection from Open-Source Re-Emerging Social Data

Bayesian Nonlinear Models for Repeated Measurement Data: An Overview, Implementation, and Applications

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