### 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 |
TBC |
TBC |

## Informal Presentations

Session 2 - Niko, Occams Razor

Session 3 - Niko, Graphical models

Session 4 - Bayesian estimation of a psychometric function

Tenenbaum talk http://techtv.mit.edu/videos/9f656eda702dd387b80edc022d6468d1262aac36/private

David Mackay talk on MCMC methods http://videolectures.net/mackay_course_12/

## Papers

Griffins 2012 (from session 2)

Rosenkrantz 1979 (from session 2)

Roweis and Ghahramani 1995 (from session 3)

Ernst and Banks 2002 (from session 3)