Dummy

BayesHive

Getting started (12m17s)

Dynamical systems (36m05s)

Coming soon: Bayesian statistics

- any model based on stochastic differential equations
- including models with unobserved variables and timeseries
- estimate parameters from historical data
**Outcomes:**probabilistic portfolio forecasts, derivatives pricing, risk estimates

- Linear and nonlinear regression, repeated measures ANOVA
- Bayesian effect size estimates
- Dynamical systems models for physics, biosignals and behaviour
- Collect analyses into a printable document
- Share your models, data and documents

- Statistical modeling
- Probability distributions
- Uncertainty and risk
- Bayesian statistics
- Dynamical systems

- Spreadsheets
- Time series
- MATLAB .MAT files
- Quandl data
- or use shared data

- Probability distributions
- Dynamical systems
- Stochastic differential equations

- run shared models

- Estimate effect sizes
- Measure parameters
- Quantify uncertainty
- Predict and forecast
- Test hypotheses
- Calculate risk
- Make optimal decisions

Wikipedia | Scholarpedia | Sivia | Gelman et al.

In **Fully Bayesian computing** one programs directly with probability
distributions to build a statistical model
for observed data. Everything else becomes trivial — and
provably optimal. That includes decisions,
forecasts, uncertainty quantification, risk assessments,
control algorithms and measurements of underlying states.

Kerman and Gelman | Wired | Beau Cronin | Microsoft

Kerman and Gelman | Wired | Beau Cronin | Microsoft

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