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  2. Modelling data

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/modelling%20data.pdf
    19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. •
  3. Modelling data

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/modelling%20data.pdf
    19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. •
  4. Predictive Automatic Relevance Determinationby Expectation…

    https://mlg.eng.cam.ac.uk/pub/pdf/QiMinPic04a.pdf
    13 Feb 2023: The first experiment has 30 random trainingpoints and 5000 random test points with dimension200. ... The estimated predictive performance is better correlated with the test errors thanevidence and sparsity.
  5. mlss2003_main.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Ras04.pdf
    13 Feb 2023: One of the primary goals computing the posterior is that it can be used tomake predictions for unseen test cases. ... n for thetraining means and analogously for the test means µ; for the covariance weuse Σ for training set covariances, Σ for
  6. 13 Feb 2023: However,it gives proper consideration to the uncertainty surrounding the test point and exactly computes themoments of the correct posterior distribution. ... 0.5log(x2(sin(2x)2)1). Figure 3: Comparison of models for suite of 6 test functions.
  7. 4F13 Machine Learning: Coursework #2: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/cw/coursework2.pdf
    19 Nov 2023: c) 10% : Using the model from question b), what will the test set log probability be if the testset B contains a word which is not contained in the training set ... e) 10% : What is the log probability for the test document with ID 2001?
  8. What Keeps a Bayesian Awake At Night? Part 1: Day Time · Cambridge…

    https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-1.html
    12 Apr 2024: Examples include inferring the mass of the Higgs boson ($X$) from collider data ($D$); estimating the prevalence of Covid 19 infections ($X$) from PCR test data ($D$); or reconstructing files ($X$) ... One way to view them is as unit tests that the
  9. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    19 Jun 2024: pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets. ... Figure 8: Approximate GP regression results on Bostondataset. Reported numbers are average test RMSE, alongwith bootstrap estimates of standard error
  10. Split and Merge EM Algorithm for Improving Gaussian Mixture Density…

    https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha00b.pdf
    13 Feb 2023: toimprove the likelihood of both the training data and of held-out test data. ... 2.In Fig. 2, the upper (lower) trajectory corresponds tothe training (test) data.
  11. FAST ONLINE ANOMALY DETECTION USING SCAN STATISTICS Ryan Turner ...

    https://mlg.eng.cam.ac.uk/pub/pdf/TurBotGha10.pdf
    13 Feb 2023: The compu-tational burden is small since the routine only needs to berun when configuring the test. ... We compareit to the CUSUM method, linear trend methods, and uni-formity tests.

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