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Replication codes for the paper "Bayesian active questionnaire design for cause-of-death assignment using verbal autopsies" by T. Yoshida, T.S. Fan, T. McCormick, Z. Wu & Z.R. Li. (2023) (The AHLI Conference on Health, Inference, and Learning (CHIL) )

yoshida-t/Bayesian-active-questionnaire-for-VA

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Introduction

This folder contains .R files for functions and experiments illustrated in the paper “Bayesian active questionnaire design for cause-of-death assignment using verbal autopsies” by T. Yoshida, T.S. Fan, T. McCormick, Z. Wu & Z.R. Li. (2023) (The AHLI Conference on Health, Inference, and Learning (CHIL) )

File Structure

  • rcodes
    • ‘functions.R’ : Contain all functions used for experiments
    • ‘0-simulate-data.R’ : Generate synthetic data (correctly specified model and misspecified model)
    • ‘1-experiment-nonstop.R’ : Conduct an experiment to compare accuracy over iterations with synthetic data
    • ‘2-experiment-stopping.R’ : Conduct an experiment to compare different stopping rules with synthetic data
    • ‘3-experiment-penalty.R’ : Conduct an experiment to compare different noise settings (‘h’) and different jumping penalization parameters (‘lambda’) with synthetic data
    • ‘4-experiment-phmrc-nonstop-CV.R’ : Conduct an experiment to compare accuracy over iterations with PHMRC data
    • ‘5-experiment-phmrc-stopping-CV.R’ : Conduct an experiment to compare different stopping rules with PHMRC data
    • ‘6-experiment-phmrc-stopping-process-CV.R’ : Conduct an experiment to compare different noise settings (‘h’) and different jumping penalization parameters (‘lambda’) with PHMRC data
    • cpp
      • ‘PWKL.cpp’: Run PWKL algorithm
    • ‘output’ : Store CV results
    • ‘table’ : Store tables for stopping rule experiments
    • ‘input_data’ : Store synthetic and PHMRC data
    • ‘fig’ : Store figures
    • ‘data’ : Dataset for PHMRC

About

Replication codes for the paper "Bayesian active questionnaire design for cause-of-death assignment using verbal autopsies" by T. Yoshida, T.S. Fan, T. McCormick, Z. Wu & Z.R. Li. (2023) (The AHLI Conference on Health, Inference, and Learning (CHIL) )

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