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101 - 110 of 370 search results for Economics test |u:mlg.eng.cam.ac.uk where 35 match all words and 335 match some words.
  1. Results that match 1 of 2 words

  2. Dependent Indian Buffet Processes Sinead Williamson Peter Orbanz…

    https://mlg.eng.cam.ac.uk/pub/pdf/WilOrbGha10.pdf
    13 Feb 2023: The data was randomly split into atraining set of 130 countries, and a test set of 14 coun-tries. ... For each test country, one randomly selectedindicator was observed, and the remainder held outfor prediction.
  3. - IB Paper 7: Probability and Statistics

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect01.pdf
    19 Nov 2023: Why do we need this, is it useful?• Make inference about uncertain events• Form the basis of information theory• Test the strength of statistical evidence. •
  4. Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf
    19 Jun 2024: 5.2 Variance-reduced ELBO estimation for deep generative models. In this section, we test GCMC sampling strategies on a deep generative modelling application. ... 10. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and
  5. Learning to Control a Low-Cost Manipulator usingData-Efficient…

    https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasFox11.pdf
    13 Feb 2023: Althoughdeposit failure feedback was not available to the learner, thedeposit success is good across 10 test trials and four differenttraining setups. ... Second, Tab. III reports the block-deposit success rates for10 test runs (and four different
  6. Gibbs sampling (an MCMC method) and relations to EM

    https://mlg.eng.cam.ac.uk/zoubin/SALD/week7at.pdf
    27 Jan 2023: Uniform U(0, θ] distribution, θ > 0. We select at random m n disks, having a common θ for failure We select n of these (at random) and test them until
  7. Probabilistic Modelling, Machine Learning,and the Information…

    https://mlg.eng.cam.ac.uk/zoubin/talks/mit12csail.pdf
    27 Jan 2023: Some Comparisons. Table 1: Test errors and predictive accuracy (smaller is better) for the GP classifier, the supportvector machine, the informative vector machine, and the sparse pseudo-inputGP classifier. ... Data set GPC SVM IVM SPGPC. name train:test
  8. Approximate inference for the loss-calibrated Bayesian

    https://mlg.eng.cam.ac.uk/pub/pdf/LacHusGha11.pdf
    13 Feb 2023: We also assume. the transductive scenario where we are given a test setS of S points {xs}Ss=1, i.e. ... of the shift between the test and trainingdistributions (columns) and the asymmetry of loss (rows).
  9. LNAI 3944 - Evaluating Predictive Uncertainty Challenge

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiRasSinetal06.pdf
    13 Feb 2023: The participants could then usethem to train their algorithms before submitting the test predictions. ... The test results were made public on December 11. The website remainsopen for submission.
  10. Bayesian Hierarchical Clustering Katherine A. Heller…

    https://mlg.eng.cam.ac.uk/zoubin/papers/bhcnew.pdf
    27 Jan 2023: As we will see, the main difference isthat our algorithm uses a statistical hypothesis test tochoose which clusters to merge. ... Sec-ond our algorithm is derived from Dirichlet processmixtures. Third the hypothesis test at the core ofour algorithm tests
  11. gppl.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/icml05chuwei-pl.pdf
    27 Jan 2023: 2.3. Prediction. Now let us take a test pair (r, s) on which the pref-erence relation is unknown. ... our algorithm. The test results of the two algorithmsare presented in the left graph of Figure 2.

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