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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. •

13 Feb 2023: There is one trainingcase (x(1); t(1)) and one test case for which we wish to predict y. ... The dotted line represents an observation y1 = t(1). In the right-hand plot we seethe distribution of the output for the test case, obtained by conditioning on

13 Feb 2023: operations to improve the likelihood of both the training data and of held-out test data. ... The data size was 200/class for training and 200/class for test.

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?

13 Feb 2023: However, the test from (SDW09) does not reflect‘smoothness’ between decisions at con-secutive time points. ... The score in the figure title is the Bayes factor of the standardGPTwoSample test.

27 Jan 2023: z Can be applied to distributions where tests of conditional independence are known, but scores aren’t. ... No Yes. X3X2. X1 X3. X2. X1. Test. Test Conditions. 49.

13 Feb 2023: 3)–(5). Doing this properlyrequires mapping uncertain test inputs through theGP dynamics model. ... b) Histogram (after 1,000 test runs)of the distances of the flywheel frombeing upright.

13 Feb 2023: new test example for the specific student weight as estimated on the given training set. ... Complex Syst. 5, 603-643. Hansen, L. 1993. Stochastic linear learning: Exact test and training error aver- ages.

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

27 Jan 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.