Diff for "CbuImaging/Bayesian_theory" - MRC CBU Imaging Wiki
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Please see below for a full list of all the formal sessions.  Please see below for a full list of all the formal sessions.
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||Session||Questions||Date||Presenters||
|| 1 || 1. [[attachment:BTIG_Session_1_240913.pptx | 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 distri
bution? 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 is the significance of the direction, relative to time, that a model operates in? 18. How can model structure be learned? 19. What is the Chinese Restaurant Process and how is it used in Bayesian clustering? 20. What is CrossCat and how does it work? || 19th Nov || Jenna Parker Yara Van Someren ||
|| 6 || 21. What are probabilistic languages and hardware? 22. What is Church and how does it work? 23. What is Infer.Net? How does it work? Can we use it? 24. 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 ||
|| 7 || 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? || 17th Dec || Alex Kuala Ian Charest ||
||Session ||Questions ||Date ||Presenters ||
||1 ||1. [[attachment:BTIG_Session_1_240913.pptx|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. [[http://imaging.mrc-cbu.cam.ac.uk/imaging/CbuImaging/Bayesian_theory?action=AttachFile&do=view&target=Bayesian+Theory+Interest+Group+Session+2.pptx|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 distribu
tion? 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 is the significance of the direction, relative to time, that a model operates in? 18. How can model structure be learned? 19. What is the Chinese Restaurant Process and how is it used in Bayesian clustering? 20. What is CrossCat and how does it work? ||19th Nov ||Jenna Parker Yara Van Someren ||
||6 ||21. What are probabilistic languages and hardware? 22. What is Church and how does it work? 23. What is Infer.Net? How does it work? Can we use it? 24. 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 ||
||7 ||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? ||17th Dec ||Alex Kuala Ian Charest ||


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|| Session || Questions || Date || Presenters ||
|| 8 || 1. How might Bayesian inference contribute to perception and action? 2. Is the direction inverted for action, because it is output? 3. How might Bayesian inference work in vision? 4. Does Bayesian inference in the brain require recurrent processing? || 7th Jan || Tom Powell Phil Pell ||
|| 9 || 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 Niko Kriegeskorte ||
|| 10 || 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? || 4th Feb || Yaara Erez Helen Blank ||
|| 11 || 13. How can neuronal populations encode probability distributions? 14. What are probabilistic population codes? 15. What is the sampling hypothesis of neuronal representation? 16. What is a Laplace code? || 18th Feb || Kristjan Kalm Jonathan O’Keeffe ||
|| 12 || 17. How can neuronal populations perform Bayesian inference? 18. How can neuronal populations perform Bayesian learning? 19. What is the neuronal evidence for representations of uncertainty? 20. What is the neuronal evidence for Bayesian inference and learning? || 4th March || Jiaxing Zhang Andrea Greve ||
|| 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 ||
||Session ||Questions ||Date ||Presenters ||
||8 ||1. How might Bayesian inference contribute to perception and action? 2. Is the direction inverted for action, because it is output? 3. How might Bayesian inference work in vision? 4. Does Bayesian inference in the brain require recurrent processing? ||7th Jan ||Tom Powell Phil Pell ||
||9 ||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 Niko Kriegeskorte ||
||10 ||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? ||4th Feb ||Yaara Erez Helen Blank ||
||11 ||13. How can neuronal populations encode probability distributions? 14. What are probabilistic population codes? 15. What is the sampling hypothesis of neuronal representation? 16. What is a Laplace code? ||18th Feb ||Kristjan Kalm Jonathan O’Keeffe ||
||12 ||17. How can neuronal populations perform Bayesian inference? 18. How can neuronal populations perform Bayesian learning? 19. What is the neuronal evidence for representations of uncertainty? 20. What is the neuronal evidence for Bayesian inference and learning? ||4th March ||Jiaxing Zhang Andrea Greve ||
||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 ||


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, with more informal sessions in between, to further explore the issues raised.

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

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

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 is the significance of the direction, relative to time, that a model operates in? 18. How can model structure be learned? 19. What is the Chinese Restaurant Process and how is it used in Bayesian clustering? 20. What is CrossCat and how does it work?

19th Nov

Jenna Parker Yara Van Someren

6

21. What are probabilistic languages and hardware? 22. What is Church and how does it work? 23. What is Infer.Net? How does it work? Can we use it? 24. 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

7

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?

17th Dec

Alex Kuala Ian Charest

Questions 2: cognitive and brain science

Session

Questions

Date

Presenters

8

1. How might Bayesian inference contribute to perception and action? 2. Is the direction inverted for action, because it is output? 3. How might Bayesian inference work in vision? 4. Does Bayesian inference in the brain require recurrent processing?

7th Jan

Tom Powell Phil Pell

9

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 Niko Kriegeskorte

10

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?

4th Feb

Yaara Erez Helen Blank

11

13. How can neuronal populations encode probability distributions? 14. What are probabilistic population codes? 15. What is the sampling hypothesis of neuronal representation? 16. What is a Laplace code?

18th Feb

Kristjan Kalm Jonathan O’Keeffe

12

17. How can neuronal populations perform Bayesian inference? 18. How can neuronal populations perform Bayesian learning? 19. What is the neuronal evidence for representations of uncertainty? 20. What is the neuronal evidence for Bayesian inference and learning?

4th March

Jiaxing Zhang Andrea Greve

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

Presentations

CbuImaging: CbuImaging/Bayesian_theory (last edited 2014-03-06 16:41:37 by JennaParker)