Bayesian Statistics Expert

Expert in Bayesian statistical methods — Bayesian A/B testing, credible intervals, prior selection, hierarchical models, and probabilistic decision-making with PyMC and Stan.

Platinum

v1.0.00 activationsData & AnalyticsTechnologyadvanced

SupaScore

85.3

Research Quality (15%)

8.7

Prompt Engineering (25%)

8.6

Practical Utility (15%)

8.3

Completeness (10%)

8.4

User Satisfaction (20%)

8.5

Decision Usefulness (15%)

8.6

Best for

▸Designing Bayesian A/B tests with credible intervals and ROPE analysis
▸Building hierarchical models for multi-level data in PyMC or Stan
▸Conducting prior elicitation workshops with domain experts
▸Implementing Bayesian time series forecasting with uncertainty quantification
▸Creating probabilistic business decision frameworks with expected loss calculations

What you'll get

●Complete PyMC model with prior predictive checks, NUTS sampling configuration, convergence diagnostics (R-hat, ESS), and posterior interpretation
●Bayesian A/B test analysis with probability of superiority, expected loss calculations, ROPE decision framework, and business recommendations
●Hierarchical model implementation with non-centered parameterization, group-level effects analysis, and shrinkage interpretation

Not designed for ↓

×Traditional frequentist hypothesis testing with p-values
×Deep learning or neural network architectures
×Classical machine learning model selection without uncertainty
×Non-parametric statistical methods or distribution-free tests

Expects

Business problem with data context, domain constraints, and decision criteria that require probabilistic reasoning and uncertainty quantification.

Returns

Complete Bayesian analysis with PyMC/Stan code, posterior interpretations, credible intervals, model diagnostics, and actionable probabilistic insights.

Evidence Policy

Standard: no explicit evidence policy.

bayesian-statisticspymcstanab-testingcredible-intervalsprobabilistic-programming

Research Foundation: 6 sources (1 books, 3 official docs, 2 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial version

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

A/B Test AnalystPlatinum Experimental Design SpecialistGold Hypothesis Testing ExpertGold Python Data AnalystGold Statistical Analysis AdvisorPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Causal Inference AnalystGold Time Series Forecasting ExpertPlatinum Monte Carlo Simulation ExpertGold

Common Workflows

Bayesian Experiment Design & Analysis

Design statistically sound experiments, implement Bayesian analysis framework, and interpret results for business decisions

Experimental Design Specialist→bayesian-statistics-expert→A/B Test Analyst

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