CbuImaging/Bayesian

Welcome to the Bayesian Theory Interest Group page.

Over the next few months the BTIG will meet weekly. Forthnightly, there will be presentations to address predetermined questions, as outlined below, with more informal sessions in between, to further explore the issues raised. Meetings will be held in the West Wing Seminar Room at the CBU. If this proves too small, we'll decamp into the Lecture Theatre.

Please see below for a full list of all the formal sessions.

The materials generated for the presentations will be added here to provide a tool for further understanding Bayesian Theory, available to all.

Below the table you will find links to any materials presented in the informal sessions and any papers shared.

Questions 1: statistics and computer science

Session	Questions	Date	Presenters
1	1. What is Bayesian inference? 2. What are prior, likelihood, and posterior? 3. What is the evidence (or marginal likelihood)? 4. What is Bayesian model selection?	24th Sept	Alex Billig
2	5. What is a probabilistic generative model? 6. What is “inference on a model”? 7. What is Bayesian inversion of a model? 8. What is the Bayesian Occam’s razor?	8th Oct	Andy Thwaite
3	9. What is a graphical model and how is it related to a multivariate probability distribution? 10. What is a Bayesian network (or Bayes net)? 11. What is “explaining away”? 12. How can Bayesian inference be performed on complex models?	22nd Oct	Kristjan Kalm
4	13. What are a Markov chain, Monte Carlo and MCMC? 14. What is a Gibbs sampler? 15. What is importance sampling? 16. What is a particle filter?	5th Nov	Charlotte Rae Seyed Kaligh-Razavi
5	17. What are probabilistic languages and hardware? 18. What is Church and how does it work? 19. What is Infer.Net? How does it work? Can we use it? 20. How can we use Bayesian inference in our data analysis? Examples? +*How is Bayesian inference implemented in other statistical programming languages, such as R?	3rd Dec	Jonathan Fawcett Fawad Jamshed

Questions 2: cognitive and brain science

Session	Questions	Date	Presenters
6.1	1.What is the significance of the direction and time that a model operates in, and does Bayesian inference in the brian require recurrent processing? 2. How might Bayesian inference work in vision?	14th Jan	Jenna Parker Yara Van Someren
6.2	3.How might Bayesian inference contribute to perception and action? 4. Is the direction inverted for action, because it is output?	14th Jan	Tom Powell Phil Pell
7	5. How might feedforward signal processing and Bayesian inference work together in the brain? 6. How is inference related to learning? 7. What is learning the learn? 8. What is the “blessing of abstraction”?	21st Jan	Marieke Mur
8	25. What is the relationship between Bayesian and frequentist inference? 26. Do we need the latter, if we do the former? 27. What’s the relationship between Bayesian inference, overfitting, and self-fulfilling analysis? 28. What are some cool applications of Bayesian inference and learning in data analysis?	28th Jan	Alex Kuala Ian Charest
9	9. What is the behavioural evidence for Bayesian inference as a model for perception? 10. ...for vision, in particular? 11. ...for decision making and cognition? 12. ...for action and sensorimotor control?	18th Feb	Yaara Erez Helen Blank
10	13. How can neuronal populations encode probability distributions? 14. What are probabilistic population codes?	25th Feb	Kristjan Kalm
11	15. How can neuronal populations perform Bayesian inference? 16. How can neuronal populations perform Bayesian learning? 17. What is the neuronal evidence for representations of uncertainty? 18. What is the neuronal evidence for Bayesian inference and learning?	4th March	Jiaxing Zhang Andrea Greve
12	19. What is the sampling hypothesis of neuronal representation? 20. What is a Laplace code?	11th March	Jonathan O’Keeffe
13	21. How can we use fMRI and neuronal recordings to test for representations of uncertainty (in object vision)? 22. ...to test for Bayesian inference? 23. ...to test for for Bayesian learning? 24. How is Bayesian inference related to predictive coding?	18th March	Arjen Alink Alex Clarke
Specialist session	25. What is the Chinese Restaurant Process and how is it used in Bayesian clustering? 26. What is CrossCat and how does it work?	TBC	TBC