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13 Feb 2023: We generated a 200-step time series of 10-dimensional data from three models:5(a) a factor analyser (i.e.

13 Feb 2023: For all datasets, we usethe smallestm = 200 eigenvalue and eigenvector pairs from the graph Laplacian. ... 200 88.1 1.3 88.0 1.3 80.4 2.5 84.4 1.6 86.0 1.5 78.3 1.3 60.8 7.3 84.3

13 Feb 2023: 0 50 100 150 200. 0. 0.2. 0.4. 0.6. 0.8. 1.

27 Jan 2023: of 20 iterations per step.50 100 150 200 250 300 350 400 450 500. ... 140. Active Learning with Statistical Modelstraining set size. 50 100 150 200 250 300 350 400 450 500.

13 Feb 2023: 100. 200. 300. 400. 500. Number of topics. Num. ber. of w. ... In bothmodels, topics appearing in more than 200 documents have beenexcluded to focus on the low frequency topics.

13 Feb 2023: 0.003. 0.01. 0.03. 0.1. 0.3. 1randomvariance. 50 100 150 200 250 300 350 400 450 500. ... 140. Active Learning with Statistical Models. training set size50 100 150 200 250 300 350 400 450 500.

13 Feb 2023: If onlylittle data is available, e.g. 200 data points for an 18 dim.model, the model learning performance can be improvedwhen using additional prior system knowledge [11]. ... The training data, e.g. 200 samples in an 18dimensional space after 4 episodes,

23 Nov 2022: None (one in progress, 200 times slower than SciPy according to the author!).

27 Jan 2023: We generated a 200-step time series of 10-dimensional data from three models:5(a) a factor analyser (i.e.

13 Feb 2023: Theanalogous priors are used for both other datasets. The EachMovie dataset was preprocessed, first by removing movies rated by less than 15people, and people who rated less than 200 movies.