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- 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, 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. ])=. -
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/4f13/1415/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
https://mlg.eng.cam.ac.uk/teaching/4f13/1213/lect0304.pdf19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>),. ... b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>). Do try this at home! -
3F3: Signal and Pattern Processing Lecture 1: Introduction to ...
https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf19 Nov 2023: 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. a. Γ(a)xa1 exp{bx}. Beta (x [0, 1]):p(x|α,β) = Γ(α β). -
- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1112/lect0304.pdf19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>),. ... b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>). Do try this at home! -
Directed and Undirected Graphical Models
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf19 Jun 2024: Z. (b). X. Y. Z. 12 / 26. D-separation (“directed separation”) in Bayesian networks. ... over A,B,C :. p(D) =a,b,c. p(A = a,B = b,C = c,D). 28 / 26. -
Junction Tree, BP and Variational Methods
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf19 Jun 2024: over A,B,C :. p(D) =. a,b,c. p(A = a,B = b,C = c,D). 8 / 32. ... c. b. a. p(D | c)p(c | b)p(b | a)p(a). -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf19 Jun 2024: ergy. ES. B. (a) W = 1. 0 0.5 10.8. 0.6. ... B. (b) W = 1.38. 0 0.5 10.4. 0.3. 0.2. 0.1. -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf19 Jun 2024: More precisely,let b′ = ⟨b, ,b⟩ Rnk0 be a k-replication of b. ... That is, forany b R0, I(b,b, ,b) = 0. In addition to the above four principles satisfied by many in-equality indices, we also focus on the following property whichis -
Unifying Orthogonal Monte Carlo Methods
https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf19 Jun 2024: Let B be a set satisfying diam(B) B for someuniversal constant B that does not depend on d (B mightbe for instance a unit sphere). ... Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B.,and Smola, A. -
Conditions Beyond Treewidth for Tightness of Higher-order LP…
https://mlg.eng.cam.ac.uk/adrian/conditions.pdf19 Jun 2024: Let P ={x Rm|Ax b} be a polytope for some A =[a1,. ... ak]. > Rkm, b Rk (for some k N). Thenfor v Ext(P), we have.
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