Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• INLA is a fast, deterministic method for Bayesian inference.
• INLA is particularly useful for large datasets and complex models.
• The R INLA package is widely used for implementing INLA methodology.
• INLA has been applied in various fields, including epidemiology and air quality control.
• Computational challenges in INLA are minimal compared to MCMC methods.
• The Smart Gradient method enhances the efficiency of INLA.
• INLA can handle various likelihoods, not just Gaussian.
• SPDs allow for more efficient computations in spatial modeling.
• The new INLA methodology scales better for large datasets, especially in medical imaging.
• Priors in Bayesian models can significantly impact the results and should be chosen carefully.
• Penalized complexity priors (PC priors) help prevent overfitting in models.
• Understanding the underlying mathematics of priors is crucial for effective modeling.
• The integration of GPUs in computational methods is a key future direction for INLA.
• The development of new sparse solvers is essential for handling larger models efficiently.

Chapters:

06:06 Understanding INLA: A Comparison with MCMC

08:46 Applications of INLA in Real-World Scenarios

11:58 Latent Gaussian Models and Their Importance

15:12 Impactful Applications of INLA in Health and Environment

18:09 Computational Challenges and Solutions in INLA

21:06 Stochastic Partial Differential Equations in Spatial Modeling

23:55 Future Directions and Innovations in INLA

39:51 Exploring Stochastic Differential Equations

43:02 Advancements in INLA Methodology

50:40 Getting Started with INLA

56:25 Understanding Priors in Bayesian Models

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad

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Today’s clip is from episode 135 of the podcast, with Teemu Säilynoja.

Alex and Teemu discuss the importance of simulation-based calibration (SBC). They explore the practical implementation of SBC in probabilistic programming languages, the challenges faced in developing SBC methods, and the significance of both prior and posterior SBC in ensuring model reliability.

The discussion emphasizes the need for careful model implementation and inference algorithms to achieve accurate calibration.

Get the full conversation here.

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Transcript

This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Visit our Patreon page to unlock exclusive Bayesian swag ;)

Takeaways:

• Teemu focuses on calibration assessments and predictive checking in Bayesian workflows.
• Simulation-based calibration (SBC) checks model implementation
• SBC involves drawing realizations from prior and generating prior predictive data.
• Visual predictive checking is crucial for assessing model predictions.
• Prior predictive checks should be done before looking at data.
• Posterior SBC focuses on the area of parameter space most relevant to the data.
• Challenges in SBC include inference time.
• Visualizations complement numerical metrics in Bayesian modeling.
• Amortized Bayesian inference benefits from SBC for quick posterior checks. The calibration of Bayesian models is more intuitive than Frequentist models.
• Choosing the right visualization depends on data characteristics.
• Using multiple visualization methods can reveal different insights.
• Visualizations should be viewed as models of the data.
• Goodness of fit tests can enhance visualization accuracy.
• Uncertainty visualization is crucial but often overlooked.

Chapters:

09:53 Understanding Simulation-Based Calibration (SBC)

15:03 Practical Applications of SBC in Bayesian Modeling

22:19 Challenges in Developing Posterior SBC

29:41 The Role of SBC in Amortized Bayesian Inference

33:47 The Importance of Visual Predictive Checking

36:50 Predictive Checking and Model Fitting

38:08 The Importance of Visual Checks

40:54 Choosing Visualization Types

49:06 Visualizations as Models

55:02 Uncertainty Visualization in Bayesian Modeling

01:00:05 Future Trends in Probabilistic Modeling

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand...