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The Objective Bayesian

Monthly newsletter devoted to reworking statistics to deliver results of the kind that we need

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Objective Bayesian

How to Engineer Default Priors for Bayesian Hypothesis Testing

Build integral priors with reference analysis for model comparison without prior knowledge

Ryan Burn

Optimize Leave-one-out Cross-validation for Lasso Regression

Feb 11, 2025

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6 min read

Optimize Leave-one-out Cross-validation for Lasso Regression

Apply a modified version of the LARS algorithm to efficiently find the exact hyperparameter that minimize leave-one-out prediction error.

Ryan Burn

How to Apply the Central Limit Theorem to Bounded Data

Dec 2, 2024

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23 min read

How to Apply the Central Limit Theorem to Bounded Data

What can we say about the mean of data distributed in an interval [a, b]?

How to use Objective Bayesian Inference to Interpret Election Polls

Oct 23, 2024

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12 min read

How to use Objective Bayesian Inference to Interpret Election Polls

How to build a polls-only objective Bayesian model that goes from state polling lead to probability of winning the state.

Ryan Burn

How to use objective Bayesian inference to compare binomial proportions

Oct 1, 2024

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21 min read

How to use objective Bayesian inference to compare binomial proportions

Derive an objective prior for the difference of two binomial distribution proportions

Ryan Burn

How to Approximate Functions with Adaptive Sparse Grids in Chebyshev Nodes

Sep 3, 2024

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23 min read

How to Approximate Functions with Adaptive Sparse Grids in Chebyshev Nodes

Use Chebyshev interpolants and adaptive sparse grids to efficiently approximate smooth functions of multiple variables

Ryan Burn

Introduction to Objective Bayesian Hypothesis Testing

Aug 17, 2024

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27 min read

Introduction to Objective Bayesian Hypothesis Testing

How to derive posterior probabilities for hypotheses using default Bayes factors

Ryan Burn

Introduction to Objective Bayesian Inference

Aug 14, 2024

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38 min read

Introduction to Objective Bayesian Inference

How to calculate probability when "we absolutely know nothing antecedently to any trials made" (Bayes, 1763)

Ryan Burn

Archive

How to Engineer Default Priors for Bayesian Hypothesis Testing

Optimize Leave-one-out Cross-validation for Lasso Regression

How to Apply the Central Limit Theorem to Bounded Data

How to use Objective Bayesian Inference to Interpret Election Polls

How to use objective Bayesian inference to compare binomial proportions

How to Approximate Functions with Adaptive Sparse Grids in Chebyshev Nodes

Introduction to Objective Bayesian Hypothesis Testing

Introduction to Objective Bayesian Inference

The Objective Bayesian

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