Learning Bayesian Statistics #110 Unpacking Bayesian Methods in AI with Sam Duffield
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Jul 10, 2024 Expert Sam Duffield discusses leveraging Bayesian methods in AI, focusing on mini-batch techniques, approximate inference, thermodynamic computing, and the Posteriors python package. He simplifies complex concepts for non-expert audiences and highlights the role of temperature in Bayesian models, stochastic gradient MCMC, and uncertainty quantification for improved predictions.
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Introduction to Bayes
- Sam Duffield was initially drawn to Bayesian statistics due to its intuitive nature and perceived mathematical simplicity.
- Bayes' theorem elegantly handles updates, requiring minimal effort: define likelihood and prior, and the theorem does the rest.
Time Series Models
- Gaussian Processes (GPs) extend static Bayesian models to continuous time, offering advantages for time series analysis.
- State-space and hidden Markov models also handle time series data efficiently by incorporating uncertainty from past observations.
Posteriors and Large-Scale AI
- Posteriors is designed for large-scale AI models, focusing on mini-batch processing for scalability.
- It prioritizes approximate inference techniques for practicality in enterprise settings.