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Journal of Statistical Software
Research Hotspot & Journal Scope - Bayesian


Journal of Statistical Software - DOI: 10.18637/JSS.V089.I09
Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC

Alireza S. Mahani · Mansour T. A. Sharabiani ·

Computer Science
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Journal of Statistical Software - DOI: 10.18637/JSS.V090.I10
bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors

Seongil Jo · Taeryon Choi · Beomjo Park · Peter J. Lenk ·

Computer Science
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We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods.

Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation [10.1515/ijb-2017-0092]


We performed a simple Bayesian analysis on the data from 68 rats, 33 of which had lesions of the medial prefrontal cortex), to examine patterns of responding in the pre-solution period.

Exacerbation of the credit assignment problem in rats with lesions of the medial prefrontal cortex is revealed by Bayesian analysis of behavior in the pre-solution period of learning [10.1016/j.bbr.2019.112037]


After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors.

Semantic Modelling of Ship Behavior in Harbor Based on Ontology and Dynamic Bayesian Network [10.3390/IJGI8030107]


To identify population and area-level factors predictive of CHW catchment area TB case notification rates, we constructed Bayesian spatially autocorrelated regression models with Poisson response distributions.

Disparities in access to diagnosis and care in Blantyre, Malawi, identified through enhanced tuberculosis surveillance and spatial analysis [10.1186/s12916-019-1260-6]


The third stage leverages a recursive Bayesian learning method and branch current state estimation residuals to estimate the daily load profiles of unobserved customers without SMs.

A Multi-Timescale Data-Driven Approach to Enhance Distribution System Observability [10.1109/TPWRS.2019.2893821]


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