Abstract: Techniques to simulate statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, where statistics of the statistical test are based on parameter vectors to follow a probability distribution, a statistic simulator component to generate statistics for the parameter vectors from the simulated data, each parameter vector represented with a single point in a grid of points, the statistic simulation component to distribute portions of the simulated data or simulated statistics across multiple nodes of a distributed computing system in accordance with a column-wise or column-wise-by-group distribution algorithm, and a code generator component to create a computational representation arranged to generate an approximate probability distribution for each point in the grid of points from the simulated statistics, the approximate probability distribution to comprise an empirical cumulative distribution function (CDF).

Abstract: Techniques to perform data reduction for statistical tests are described. An apparatus may comprise an evaluation component to receive a computational representation arranged to generate an approximate probability distribution for statistics of a statistical test, the computational representation to include a simulated data structure with information for estimated cumulative distribution function (CDF) curves for one or more parameter vectors of the statistical test, each parameter vector represented with a single point in a grid of points, the evaluation component to evaluate the simulated data structure to determine whether any points in the grid of points are removable from the simulated data structure with a target level of precision, and a data reduction generator to reduce the simulated data structure in accordance with the evaluation to produce a reduced simulated data structure having a smaller data storage size relative to the simulated data structure. Other embodiments are described and claimed.

Abstract: Techniques to provide significance for statistical tests are described. An apparatus may comprise a data handler component to receive a real data set from a client device, the real data set to comprise data representing at least one measurable phenomenon, a statistical test component to receive a computational representation arranged to generate an approximate probability distribution for statistics of a statistical test based on a parameter vector, the statistics of the statistical test to follow a probability distribution, generate statistics for the statistical test using the real data set, generate the approximate probability distribution of the computational representation, and a significance generator component to generate a set of statistical significance values for the statistics through interpolation using the approximate probability distribution, the set of statistical significance values comprising one or more p-values. Other embodiments are described and claimed.

Abstract: Techniques to perform interpolation for statistical tests are described. An apparatus may comprise processor circuitry and a simulated data component for execution by the processor circuitry to generate simulated data for a statistical test, statistics of the statistical test based on parameter vectors to follow a probability distribution. The apparatus may further comprise a statistic simulator component for execution by the processor circuitry to simulate statistics for the parameter vectors from the simulated data, each parameter vector represented with a single point in a grid of points. The apparatus may further comprise a code generator component for execution by the processor circuitry to remove selective points from the grid of points to form a subset of points, and generate interpolation code to interpolate a statistic of the statistical test on any point. Other embodiments are described and claimed.

Abstract: Techniques to perform curve fitting for statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, the statistical test based on parameter vectors to follow a probability distribution. The apparatus may further comprise a statistic simulator component to simulate statistics for the parameter vectors from the simulated data, each parameter vector represented with a single point in a grid of points, calculate quantiles for the parameters vectors from the simulated data, and fit an estimated cumulative distribution function (CDF) curve to quantiles for each point in the grid of points using a monotonic cubic spline interpolation technique in combination with a transform to satisfy a defined level of precision. Other embodiments are described and claimed.

Abstract: Techniques to manage virtual classes for statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, statistics of the statistical test based on parameter vectors to follow a probability distribution, a statistic simulator component to simulate statistics for the parameter vectors from the simulated data with a distributed computing system comprising multiple nodes each having one or more processors capable of executing multiple threads, the simulation to occur by distribution of portions of the simulated data across the multiple nodes of the distributed computing system, and a distributed control engine to control task execution on the distributed portions of the simulated data on each node of the distributed computing system with a virtual software class arranged to coordinate task and sub-task operations across the nodes of the distributed computing system. Other embodiments are described and claimed.

Abstract: Techniques for estimated compound probability distribution are described. An apparatus may comprise a configuration component, perturbation component, sample generation controller, an aggregation component, a distribution fitting component, and statistics generation component. The configuration component may be operative to receive a compound model specification and candidate distribution definition. The perturbation component may be operative to generate a plurality of models from the compound model specification. The sample generation controller may be operative to initiate the generation of a plurality of compound model samples from each of the plurality of models. The distribution fitting component may generate parameter values for the candidate distribution definition based on the compound model samples. The statistics generation component may generate approximated aggregate statistics. Other embodiments are described and claimed.

Abstract: Techniques to simulate statistical tests are described. An apparatus may comprise a simulated data component to generate simulated data for a statistical test, where statistics of the statistical test are based on parameter vectors to follow a probability distribution, a statistic simulator component to generate statistics for the parameter vectors from the simulated data, each parameter vector represented with a single point in a grid of points, the statistic simulation component to distribute portions of the simulated data or simulated statistics across multiple nodes of a distributed computing system in accordance with a column-wise or column-wise-by-group distribution algorithm, and a code generator component to create a computational representation arranged to generate an approximate probability distribution for each point in the grid of points from the simulated statistics, the approximate probability distribution to comprise an empirical cumulative distribution function (CDF).

Abstract: Various embodiments are generally directed to techniques for producing statistically correct and efficient combinations of multiple simulated posterior samples from MCMC and related Bayesian sampling schemes are described. One or more chains from a Bayesian posterior distribution of values may be generated. It may be determine whether the one or more chains have reached stationarity through parallel processing on a plurality of processing nodes. Based upon the determination, each of the one or more chains that have reached stationarity through parallel processing on the plurality of processing nodes may be sorted. The one or more sorted chains may be resampled through parallel processing on the plurality of processing nodes. The one or more resampled chains may be combined. Other embodiments are described and claimed.

Abstract: Various embodiments are directed to techniques for selecting a subset of a set of simulated samples. A computer-program product including instructions to cause a computing device to order a plurality of UPDFs by UPDF value, wherein the plurality of UPDFs is associated with a chain of draws of a set of simulated samples, wherein each draw comprises multiple parameters and the UPDF values map to parameter values of the parameters; select a subset of the plurality of UPDFs based on the subset of the plurality of UPDFs having UPDF values within a range corresponding to a range of parameter values to include in a subset of the set of simulated samples; and transmit an indication of a draw comprising parameters having parameter values to include in the subset of the set of simulated samples, wherein the indication identifies the draw by associated UPDF. Other embodiments are described and claimed.

Abstract: Techniques for automated Bayesian posterior sampling using Markov Chain Monte Carlo and related schemes are described. In an embodiment, one or more values in a stationarity phase for a system configured for Bayesian sampling may be initialized. Sampling may be performed in the stationarity phase based upon the one or more values to generate a plurality of samples. The plurality of samples may be evaluated based upon one or more stationarity criteria. The stationarity phase may be exited when the plurality of samples meets the one or more stationarity criteria. Other embodiments are described and claimed.

Abstract: Techniques for automated Bayesian posterior sampling using Markov Chain Monte Carlo and related schemes are described. In an embodiment, one or more values in an accuracy phase for a system configured for Bayesian sampling may be initialized. Sampling may be performed in the accuracy phase based upon the one or more values to generate a plurality of samples. The plurality of samples may be evaluated based upon one or more accuracy criteria. The accuracy phase may be exited when the plurality of samples meets the one or more accuracy criteria. Other embodiments are described and claimed.

Abstract: Various embodiments are directed to techniques for deriving a sample representation from a random sample. A computer-program product includes instructions to cause a first computing device to fit an empirical distribution function to a marginal probability distribution of a variable within a first sample portion of a random sample to derive a partial marginal probability distribution approximation, wherein the random sample is divided into multiple sample portions distributed among multiple computing devices; fit a first portion of a copula function to a multivariate probability distribution of the first sample portion, wherein the copula function is divided into multiple portions; and transmit an indication of a first likelihood contribution of the first sample portion to a coordinating device to cause a second computing device to fit a second portion of the copula function to a multivariate probability distribution of a second sample portion. Other embodiments are described and claimed.

Abstract: Systems and methods are provided for a computer-implemented method for automatically generating a weighted average forecast model that includes receiving a plurality of forecasting models and time series data. At least one parameter of each of the received forecasting models is optimized utilizing the received time series data. A weighting factor is generated for each of the plurality of optimized forecasting models utilizing an information criteria value indicating fit quality of each of the optimized forecasting models, and the generated weighting factors are stored.

Abstract: Systems and methods are provided for a computer-implemented method for automatically generating a weighted average forecast model that includes receiving a plurality of forecasting models and time series data. At least one parameter of each of the received forecasting models is optimized utilizing the received time series data. A weighting factor is generated for each of the plurality of optimized forecasting models utilizing an information criteria value indicating fit quality of each of the optimized forecasting models, and the generated weighting factors are stored.

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Abstract: A chrysanthemum plant named ‘Lydia’ characterized by its medium sized blooms with yellow ray florets and a yellow center, with prolific branching; blooming for a period of 5 weeks.

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Abstract: A chrysanthemum plant named ‘Tatoi’ characterized by its medium sized blooms with bronze ray florets and prolific branching; natural season flower date September 13-18; blooming for a period of 5 weeks.

Abstract: A chrysanthemum plant named Libra characterized by its medium sized blooms with white ray florets and a cream center, and prolife branching; natural season flower date August 29-September 4; blooming for a period of 5 weeks.