[go: up one dir, main page]

Pyrcz et al., 2018 - Google Patents

Transforming data to a gaussian distribution

Pyrcz et al., 2018

View PDF
Document ID
18297247142745750983
Author
Pyrcz M
Deutsch C
Deutsch J
Publication year
Publication venue
Geostatistics Lessons

External Links

Snippet

Parametric models sometimes relate to an underlying theory, for example, the Gaussian distribu‐tion is the limit distribution for the sum of many independent random variables. Although some variables can be qualitatively described by similarities to parametric …
Continue reading at geostatisticslessons.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F17/30 and subgroups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F7/00—Methods or arrangements for processing data by operating upon the order or content of the data handled
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202—Market predictions or demand forecasting

Similar Documents

Publication Publication Date Title
Pyrcz et al. Transforming data to a gaussian distribution
Mo et al. Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
Kanevski et al. Environmental data mining and modeling based on machine learning algorithms and geostatistics
Wilson et al. Pathwise conditioning of Gaussian processes
Segura et al. Metamodel-based seismic fragility analysis of concrete gravity dams
Stewart et al. Non-ergodic site response in seismic hazard analysis
Scherbaum et al. Model selection in seismic hazard analysis: An information-theoretic perspective
Taflanidis et al. An efficient framework for optimal robust stochastic system design using stochastic simulation
US7653522B2 (en) Robustness optimization system
Busby Hierarchical adaptive experimental design for Gaussian process emulators
Zhong et al. Surrogate modeling of structural seismic response using probabilistic learning on manifolds
Fukushima et al. Physics‐informed neural networks for fault slip monitoring: Simulation, frictional parameter estimation, and prediction on slow slip events in a spring‐slider system
Newman et al. A survey of linear and mixed-integer optimization tutorials
Ning et al. Convolutional variational autoencoder for ground motion classification and generation toward efficient seismic fragility assessment
Chok et al. Neural network prediction of the reliability of heterogeneous cohesive slopes
Svalova et al. Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference
Ning et al. Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning
Van Elk et al. Improved uncertainty management in field development studies through the application of the experimental design method to the multiple realisations approach
Rundle et al. Nowcasting earthquakes with stochastic simulations: Information entropy of earthquake catalogs
Silva et al. A multiple training image approach for spatial modeling of geologic domains
Chen et al. Scalable physics-based maximum likelihood estimation using hierarchical matrices
Rumsey et al. Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation
Cordua et al. Improving the pattern reproducibility of multiple-point-based prior models using frequency matching
Fursov et al. Applying kriging proxies for Markov chain Monte Carlo in reservoir simulation
Qi IC design analysis, optimization and reuse via machine learning