| AffinityPropagationClusteringInterface.m |
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Cluster vectors using Frey's affinity-propagation algorithm. |
| AssignStatsConstants.m |
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A script assigining several statistical constants used by other functions |
| DisplayPCA.m |
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Display first principal components of data |
| FindDistWithGivenKL.m |
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Compute a distribution with a given KL distance from P |
| FindIndependentDistWithGivenKLFromUniform.m |
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Compute an indep. dist. with a given KL distance from uniform |
| GenerateLibHTML.m |
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Make automatically an html file telling where exactly are all functions. |
| KL_distances_rand_points.m |
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Compute distances between many pairs of 'random' points (distributions). |
| NextBestTree.m |
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Computes the 'next best' spanning tree |
| NormalizeData.m |
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Normalization - Subtracts the mean of each row and divide by standard deviation (from Tal Shay) |
| Phi.m |
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Standard Gaussian Phi |
| PhiInv.m |
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Inverse standard Gaussian Phi |
| PolishMean.m |
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Calculate mean after without lower/upper t
|
| add_noise_to_vec.m |
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Adds white gaussian noise to a vector |
| bernoulli_sum_prob.m |
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Compute the probability distribution of sum of N independent bernoulli random |
| cdf_hist.m |
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Compute cdf(t) for a histogram |
| chinese_restaurant_process.m |
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Gerenate an instance of chinese restaurant process |
| cond_mean.m |
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Compute conditional mean of a distribution given that value is > alpha |
| convolution_prob.m |
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Return the convolution of two probabilities: p+q-2pq |
| cumsum_hist.m |
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Perform cumulative summation for a histogram |
| decide_success_or_failure_online.m |
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Internal function for deciding when to stop trying (if number of failures |
| derich_correct.m |
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Add a relative derichlet correction |
| displaysamplespca.m |
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Display a PCA of the samples. |
| distributions_diff.m |
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Compute the difference between two distributions. |
| distrnd.m |
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Simulate data from any distribution (like normrnd but for general dists.) |
| diststat.m |
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Compute mean and var of a general distribution |
| enrichment_fdr_plot.m |
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Plot the 'enrichment' FDR when comparing two sets, |
| entropy.m |
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Compute entropy (in log base 2) |
| ewen_sampling_formula.m |
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Generate a sample from Ewen's distribution |
| gauss_smooth.m |
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Simple 1d Gaussian smoothing of a histogram. |
| generate_points.m |
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Generate a set of points on the plane |
| h.m |
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Binary entropy function |
| hypergeometric_for_many_sets.m |
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Give hypergeometric-like p-value for pairwise intersections of many sets |
| hypergeometric_for_three_sets.m |
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Give hypergeometric p-value for 3-way intersection, given pairwise intersections |
| indian_buffet_process.m |
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Gerenate an instance of chinese restaurant process |
| integral_hist.m |
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Perform integral for a histogram (we use simple rectangle integration) |
| integral_hist2d.m |
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Perform two-dimensional integral for a histogram (we use simple rectangle integration) |
| isbimodal_hist.m |
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Test if a distribution is bimodal |
| kurtosis_hist.m |
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Compute kurtosis for a histogram |
| log_binom.m |
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Compute log of binomial coefficient N over M. |
| log_factorial_vec.m |
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Compute log-factorial for a vector. |
| maxnormcdf.m |
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Cumulative distribution function of maximum of N gaussians |
| maxnormpdf.m |
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Density of maximum of N standard Gaussians |
| maxnormstat.m |
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Compute mean and st.d. of maximum of N standard Gaussians |
| mean_hist.m |
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Compute mean for a histogram |
| mean_single.m |
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A function for computing the mean of a large single array that |
| med_hist.m |
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Compute median for a histogram |
| median_hist.m |
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Compute median for a histogram |
| moment_hist.m |
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Compute central moment of any order for a histogram |
| my_cov.m |
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Compute covariance for two matrices |
| my_least_squares.m |
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Find the best a and b such that the sum of squares of y - (ax+b) is minimized |
| my_quantile.m |
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Like Matlab's quantile but enables to get quantiles outside [0,1]. |
| my_smooth.m |
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Performs moving sum (default)/average |
| ncx2power_to_ncp.m |
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Compute the non-centrality parameter needed to achieve a desired power |
| normalize_hist.m |
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Normalize a histogram to have sum one |
| normalize_hist2d.m |
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Normalize a two-dimensional histogram to have sum one |
| plane_check.m |
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Check if certain points are on the same plain |
| poisson_point_process_rnd.m |
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Generate data from a Poisson point process in the cube [0,1]^n |
| posdefrnd - Copy.m |
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Sample a semi positive-definite matrix |
| posdefrnd.m |
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Sample a semi positive-definite matrix |
| powernormstat.m |
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compmute moments of |z|^p when p is standard Gaussian |
| pvalPearson.m |
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VALPEARSON Tail probability for Pearson's linear correlation. |
| quantile_hist.m |
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Compute quantile for a histogram |
| quantile_normalize.m |
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Perform quantile normalization on a matrix. |
| rand_nchoosek.m |
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Pick at random k indices out of n |
| rand_normalized.m |
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Randomize an mXn matrix such that each column sums to 1 |
| rand_seed.m |
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A script for seeding Matlab's rand |
| randcircle.m |
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Draw random points in a circle |
| random_Tree_to_point.m |
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Get the closest point on a random tree (in KL) to a point (distribution). |
| randsphere.m |
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Generate random points inside a 3-d sphere |
| ranksum1side.m |
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Wilcoxon rank sum test but with one-sided p-value |
| rationormpdf.m |
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Ratio of two Gaussians density distriburion |
| rationormstat.m |
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First two moments of ratio of gaussians |
| relative_entropy.m |
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Compute the relative entropy between distributions P and Q |
| sample_gaussian_integral.m |
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A Gaussin integral |
| set_windows_pval.m |
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Assume n variables are uniformly distributed on [1, N] |
| set_windows_pval_old.m |
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Assume n variables are uniformly distributed on [1, N] |
| simple_binom_windows_pval.m |
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Assume n variables are uniformly distributed on [1, N] |
| simulate_by_normalization.m |
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Simulate uniform r.v.s. normalized by their sum |
| simulate_correlated_bernoulli.m |
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Simulates a set of correlated bernoulli random variables |
| simulate_correlated_gaussians.m |
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Simulates a set of correlated gaussian random |
| simulate_rand_points.m |
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A script for simulating throwing random points in the unit interval |
| skewness_hist.m |
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Compute skewness for a histogram |
| std_hist.m |
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Compute std for a histogram |
| std_to_q_binary.m |
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Compute Bernoulli probability for a given standard deviation |
| sum_hist.m |
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Add values in two histograms |
| table_to_marginal_probs.m |
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Compute product of marginals for a probability table |
| test_KL_distances_rand_points.m |
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Script for runnning the test_KL_distances_rand_points function |
| test_MI_computation.m |
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Test inference and mutual information computation for trees. |
| test_chisquare.m |
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Test for conditional indepndence via chi-square test (from Kevin Murphy's BNT) |
| test_correlated_gaussians.m |
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Generate correlated gaussians. Then perform de-convulution |
| test_distributions_diff.m |
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A script for testing the distributions_diff function |
| test_goodness_of_fit.m |
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function test_goodness_of_fit() |
| test_hypergeometric_for_many_sets.m |
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Test the function hypergeometric_for_many_sets |
| test_hypergeometric_for_three_sets.m |
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Give p-value for the hypergeometric score of intersection of three sets, |
| test_hypergeometric_scores.m |
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Test all hypergeometric functions |
| test_probit.m |
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Run a test of matlab probit regression. Determine coefficients, simulate |
| test_windows_pval.m |
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Test the p-values for significance, using both windows methods, |
| uniform_log_sum_reciprocal_mean.m |
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Compute the mean of -1/(sum_{i} log(p_i)) |
| uniform_spanning_tree.m |
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Generate a uniform spanning tree on n vertices |
| uniform_sum_reciprocal_mean.m |
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Compute the mean of 1/(sum_{i} p_i) |
| var_hist.m |
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Compute variance for a histogram |
| weighted_hist.m |
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Builds a histogram by binning the elements of val into containers, one |
| weighted_mean.m |
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Compute weighted average |
| weighted_rand.m |
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Produce random numbers drawn according to some weights. |
| stats.tgz |
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Download the whole package. (104 .m files) |
| fdr |
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Go to the fdr package. |
| fdr/additional_src |
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Go to the additional_src package. |
| hmp |
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Go to the hmp package. |
| mog |
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Go to the mog package. |