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  2. Background material crib-sheet Iain Murray , October 2003 Here ...

    https://mlg.eng.cam.ac.uk/teaching/4f13/cribsheet.pdf
    19 Nov 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.
  3. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/gaussian%20process.pdf
    19 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. ])=.
  4. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1819/gaussian%20process.pdf
    19 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. ])=.
  5. Background material crib-sheet Iain Murray , October 2003 Here ...

    https://mlg.eng.cam.ac.uk/zoubin/course04/cribsheet.pdf
    27 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.
  6. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/lect0304.pdf
    19 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!
  7. paper.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/nips96.pdf
    27 Jan 2023: b b b. q qz z z z1 2 3 T. ... in the decision tree at the precedingmoment in time; (b) as an HMM in which the state variable at each moment intime is factorized (cf.
  8. Junction Tree, BP and Variational Methods

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf
    19 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).
  9. 19 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.
  10. Hidden Markov decision treesMichael I. Jordan�, Zoubin Ghahramaniy,…

    https://mlg.eng.cam.ac.uk/pub/pdf/JorGhaSau96a.pdf
    13 Feb 2023: b b b. q qz z z z1 2 3 T. ... ihoo. d. b). 1. 2. 3. Figure 4: a) Articial time series data.
  11. 4F13: Machine Learning Lectures 6-7: Graphical Models Zoubin…

    https://mlg.eng.cam.ac.uk/zoubin/ml06/lect6-7.pdf
    27 Jan 2023: Z =XaA. XbB. XcC. XdD. XeE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). ... Undirected Graphical Models. A. C. B. D. E. P (A, B, C, D, E) =1Z.
  12. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    19 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
  13. nlds-final.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/GhaRow98a.pdf
    13 Feb 2023: h>1 h>. 2 : : : h>. I A> B> b>]. >. ... In This Volume.MIT Press, 1999. [2] A.P. Dempster, N.M. Laird, and D.B.
  14. zgl.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf
    27 Jan 2023: T _ b: _ b T. T _ b. T _ b is the entropy of the field at the individual unlabeled datapoint b. ... The gradient is computed as [ W! VK X j. T _ b_ b: _ b [ W (12)where the values. _
  15. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 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.
  16. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    19 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.
  17. nlds-ftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/nlds-ftp.pdf
    27 Jan 2023: A> B> b>]> [1(x) 2(x) : : : I(x) x u 1] :Then, the objective can be writtenmin;Q 8<:Xj (z )>Q1(z )j J ln jQj9=; : (8). ... In This Volume.MIT Press, 1999.[2] A.P. Dempster, N.M. Laird, and D.B.
  18. main.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Sch09b.pdf
    13 Feb 2023: 13);and third,b is generated from a constrained Gaussian analogous to Eq. ... b) Themixture data consists of 4000 imagesof two mixed digits (20 examples shown).
  19. 13 Feb 2023: In detail, ifp(x) = N (b, B) and the Gaussian kernels on the data points areN (ai = x(i), A = diag(w21,. ,
  20. Adversarial Graph Embeddings for Fair Influence Maximization over…

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf
    19 Jun 2024: distributions of nodes from A and B in the embedding space(to have |UA||A|. ... Group AGroup BGroup AGroup B. (b) The fractions of influencednodes in the two groups.
  21. Unsupervised Learning∗ Zoubin Ghahramani† Gatsby Computational…

    https://mlg.eng.cam.ac.uk/zoubin/course05/ul.pdf
    27 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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