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A Case-Study of Sample-Based Bayesian Forecasting Algorithms

For a Bayesian, real-time forecasting with the posterior predictive distribution can be challenging for a variety of time series models. First, estimating the parameters of a time series model can be difficult with sample-based approaches when the …

The Most Difference in Means: A Statistic for Null and Near-Zero Results

Two-sample p-values test for statistical significance. Yet p-values cannot determine if a result has a negligible (near-zero) effect size, nor compare evidence for negligibility among independent studies. We propose the most difference in means …

A Short Introduction to PF: A C++ Library for Particle Filtering

Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters

This paper introduces the particle swarm algorithm, a recursive and embarrassingly parallel algorithm that targets an approximation to the sequence of posterior predictive distributions by averaging expectation approximations from many particle …

PF: A C++ Library for Fast Particle Filtering

A Pseudo-Marginal Metropolis-Hastings Algorithm for Estimating Generalized Linear Models in t he Presence of Missing Data

A Factor Stochastic Volatility Model with Markov-Switching Panic Regimes