Description
Bayesian BIN (Bias, Information, Noise) Model of Forecasting.
Description
A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" <doi:10.1287/mnsc.2020.3882>.
README.md
BINtools
The goal of BINtools is to implement a BIN model, a Bayesian approach to decomposing forecasting accuracy into three components: bias, partial information, and noise.
Installation
You can install the released version of BINtools from CRAN with:
install.packages("BINtools")
Example
This is a basic example which shows you how to solve a common problem:
library(BINtools)
# An example with two forecasting groups
# a) Simulate synthetic data:
synthetic_data = simulate_data(list(mu_star = -0.8,mu_0 = -0.5,mu_1 = 0.2,gamma_0 = 0.1,
gamma_1 = 0.3, rho_0 = 0.05,delta_0 = 0.1, rho_1 = 0.2, delta_1 = 0.3,rho_01 = 0.05), 300,100,100)
# b) Estimate the BIN-model on the synthetic data:
full_bayesian_fit = estimate_BIN(synthetic_data$Outcomes,synthetic_data$Control,synthetic_data$Treatment,warmup = 1000, iter = 2000)
# c) Analyze the results:
complete_summary(full_bayesian_fit)