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Bridge sampling

Rdata data <- read.csv("~/Williams2017/Koechlin1997.csv") X1 <- data[data$C=="1",] X1 <- X1$X N1 <- length(X1) X2 <- data[data$C=="2",] X2 <- X2$X N2 <- length(X2) Stan options(mc.cores = parallel::detectCores()) library(rstan) rstan_opt

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    • Posterior probability distributions

      Rdata data <- read.csv("Koechlin1997.csv") X1 <- data[data$C=="1",] X1 <- X1$X N1 <- length(X1) X2 <- data[data$C=="2",] X2 <- X2$X N2 <- length(X2) Stan options(mc.cores = parallel::detectCores()) library(cmdstanr) library(rstan) rstan_

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      • BayesFactor: R package

        data (Koechlin et al., 1997)X1 <- c(3.3, 1, 2, 1.7, 3.7, 5.7, 3.7, 0.7, 4.7, 3.7, 7, 1.7, 6.3, 3.7, 3.3, 2.3, 4.7, 6.7) N1 <- length(X1) X2 <- c(6, 5.7, 6, 3.7, 5, 9, 6, 9, 2.3, 4.7, 5.7, 7.7, 6.7, 4.7, 5.7, 3.3, 6, 5, 6, 5, 5.3) N2 <- leng

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      Bridge sampling

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