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Practical Probabilistic Programming with Monads
https://mlg.eng.cam.ac.uk/pub/pdf/SciGhaGor15.pdf13 Feb 2023: Specifically, consider the followingdesign. data PDist a whereReturn :: a -> PDist aPBind :: PDist b -> (b -> PDist a) -> PDist aPrimitive :: Sampleable d => d a -> PDist a. ... data CDist a wherePD :: PDist a -> CDist aCBind :: CDist b -> (b -> PDist a) -
��������� �� ����������������������� �!�#"$ �%�…
https://mlg.eng.cam.ac.uk/zoubin/papers/Eric04TR.pdf27 Jan 2023: 6ûý 3. 68òBð¡ñ1ðEþ)ú¡ÿ,ÆðJõú¡ïxð1òÃújúùóÐÐóÆÐðlú:jóøWÐûA 3. 6jûòBð!Æÿ,Æðô =. bdc (û/A3! -. 6 m x #"!3!$I6 "!3 O$I6 %B&%53'$ 6bdc3 (û/A3! -. -
LNAI 3176 - Unsupervised Learning
https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf13 Feb 2023: Sum-ming out C leads to P (A, B) = P (A)P (B). ... c P (C|A = a, B = a)),and continue this procedure until all variables are assigned values. -
Unsupervised Learning∗ Zoubin Ghahramani† Gatsby Computational…
https://mlg.eng.cam.ac.uk/zoubin/papers/ul.pdf27 Jan 2023: C. B. D. E. Figure 1: Three kinds of probabilistic graphical model: undirected graphs, factor graphs and directed graphs. ... P (A, B, C, D, E) = c g1(A, C)g2(B, C, D)g3(C, D, E) (28). -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/1617/gaussian%20process.pdf19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=. -
Background material crib-sheet Iain Murray , October 2003 Here ...
https://mlg.eng.cam.ac.uk/zoubin/ml06/cribsheet.pdf27 Jan 2023: P (A = a|B = b) is the probability A = a occurs given the knowledge B = b. ... Note. a P (A = a, B = b|H) =. P (B = b|H) gives the normalising constant of proportionality. -
4F13 Machine Learning: Coursework #2: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1112/cw/coursework2.pdf19 Nov 2023: How many documents, how many words and how many unique words are there inA, in B and in the union of A and B? ... What is the per-wordperplexity? What is the per-word perplexity over all documents in B? -
4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1213/cw/coursework3.pdf19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... What is theper-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial? -
4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1617/cw/coursework3.pdf19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... Whatis the per-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial? -
4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/cw/coursework3.pdf19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... Whatis the per-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial? -
(Multivariate) Gaussian (Normal) Probability Densities
https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20and%20matrix%20equations.pdf19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x) = N(a, A),. ... b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. -
paper.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/Gha01a.pdf13 Feb 2023: 7. ". &:. 1 "Q ". # ()! $. "Q " ' " ;.,< " %! #. %? &9? B? %. 4. " #. &? &? & ,. 5 1 -#? #? %& % &? $? % P & %P&. ... B< UA A- C DA+92+& ;#MM""" M/ MM92+&/< G D ( 0 4992. -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect04.pdf19 Nov 2023: E(a) (b). Two types of nodes:• The circles in a factor graph. ... Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect04.pdf19 Nov 2023: Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). ... A. D. C. B. E. A. D. C. B. E(a) (b) (c). -
- Machine Learning 4F13, Michaelmas 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0304.pdf19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x) = N(a, A),. ... b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. -
4F13: Machine Learning Lectures 1-2: Introduction to Machine Learning …
https://mlg.eng.cam.ac.uk/zoubin/ml06/lect1-2.pdf27 Jan 2023: 1 θ = 1 and θ 0. Some distributions (cont). Uniform (x [a, b]):. ... p(x|a, b) ={. 1ba if a x b0 otherwise. Gamma (x 0):p(x|a, b) = b. -
- Machine Learning 4F13, Spring 2014
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0304.pdf19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fi, f<i) = N([ a. b. ],[A B. B> C. ])= -
- Machine Learning 4F13, Spring 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0304.pdf19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=. -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20process.pdf19 Nov 2023: p(x, y) = p([ x. y. ])= N. ([ ab. ],[. A B. B> C. ]),. we get the marginal distribution of x, p(x) by. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=. -
Unsupervised Learning∗ Zoubin Ghahramani† Gatsby Computational…
https://mlg.eng.cam.ac.uk/zoubin/course04/ul.pdf27 Jan 2023: C. B. D. E. Figure 1: Three kinds of probabilistic graphical model: undirected graphs, factor graphs and directed graphs. ... P (A, B, C, D, E) = c g1(A, C)g2(B, C, D)g3(C, D, E) (28).
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