Tabla de Contenidos: “…2.5 The Van der Waals Equation of State (EOS)2.6 The Criterion of the Thermal Energy kT for Gauging the Strength of an Interaction; 2.7 Classification of Forces and Pair Potentials; 2.8 Theoretical Analyses of Multimolecular Systems: Continuum and Molecular Approaches; 2.9 Molecular Approaches via Computer Simulations: Monte Carlo (MC) and Molecular Dynamics (MD); 2.10 Newton's Laws Applied to Two-Body Collisions; 2.11 Kinetic and Statistical Aspects of Multiple Collisions: the Boltzmann Distribution; Problems and Discussion Topics…”

Tabla de Contenidos: “…RMAN Incremental BackupsRMAN Restores; Smart Scan Prerequisites; Full Scans; Direct Path Reads; Exadata Storage; Smart Scan Disablers; Simply Unavailable; Reverting to Block Shipping; Skipping Some Offloading; How to Verify That Smart Scan is Happening; 10046 Trace; Performance Statistics (vsessstat); Offload Eligible Bytes; SQL Monitoring; Parameters; Summary; CHAPTER 3 Hybrid Columnar Compression; Oracle Storage Review; Oracle Compression Mechanisms; BASIC; OLTP; HCC; HCC Mechanics; HCC Performance; Load Performance; Query Performance; DML Performance; Expected Compression Ratios…”

Tabla de Contenidos: “…Calculate Descriptive Statistics…”

Tabla de Contenidos: “…Are we reaching the end of the positioning of statistics as a science of large numbers? -- 2.2.2. …”

Tabla de Contenidos: “…Deploying Your Model in ProductionDeploying Your Model into Staging; Testing the Web Service; Moving Your Model from Staging into Production; Accessing the Azure Machine Learning Web Service; Summary; Chapter 3: Integration with R; R in a Nutshell; Building and Deploying Your First R Script; Using R for Data Preprocessing; Using a Script Bundle (Zip); Building and Deploying a Decision Tree Using R; Summary; Part2: Statistical and Machine Learning Algorithms; Chapter 4: Introduction to Statistical and Machine Learning Algorithms; Regression Algorithms; Linear Regression; Neural Networks…”

Tabla de Contenidos: “…Gross and Joshua M. Jansa -- Statistical inference in political networks research / Bruce A. …”

Tabla de Contenidos: “…Cover -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Getting Started with Python Libraries -- Installing Python 3 -- Installing data analysis libraries -- On Linux or Mac OS X -- On Windows -- Using IPython as a shell -- Reading manual pages -- Jupyter Notebook -- NumPy arrays -- A simple application -- Where to find help and references -- Listing modules inside the Python libraries -- Visualizing data using Matplotlib -- Summary -- Chapter 2: NumPy Arrays -- The NumPy array object -- Advantages of NumPy arrays -- Creating a multidimensional array -- Selecting NumPy array elements -- NumPy numerical types -- Data type objects -- Character codes -- The dtype constructors -- The dtype attributes -- One-dimensional slicing and indexing -- Manipulating array shapes -- Stacking arrays -- Splitting NumPy arrays -- NumPy array attributes -- Converting arrays -- Creating array views and copies -- Fancy indexing -- Indexing with a list of locations -- Indexing NumPy arrays with Booleans -- Broadcasting NumPy arrays -- Summary -- References -- Chapter 3: The Pandas Primer -- Installing and exploring Pandas -- The Pandas DataFrames -- The Pandas Series -- Querying data in Pandas -- Statistics with Pandas DataFrames -- Data aggregation with Pandas DataFrames -- Concatenating and appending DataFrames -- Joining DataFrames -- Handling missing values -- Dealing with dates -- Pivot tables -- Summary -- References -- Chapter 4: Statistics and Linear Algebra -- Basic descriptive statistics with NumPy -- Linear algebra with NumPy -- Inverting matrices with NumPy -- Solving linear systems with NumPy -- Finding eigenvalues and eigenvectors with NumPy -- NumPy random numbers -- Gambling with the binomial distribution -- Sampling the normal distribution…”

Tabla de Contenidos: “…Log-Linear Models for Categorical Variables -- Regression Models for Categorical Variables -- Models for Event Histories -- Models for Multiple-Item Measurement -- Nonlinear, Nonadditive Models -- Geometric and Algebraic Models -- Scaling -- Ordered Factorial Systems -- Clustering -- Network Models -- STATISTICAL INFERENCE AND ANALYSIS -- Causal Inference -- New Statistical Techniques -- Internal Resampling -- Robust Techniques -- Many Interrelated Parameters -- Missing Data -- Computing -- Computer Packages and Expert Systems -- Exploratory Analysis and Graphic Presentation -- Combining Evidence -- OPPORTUNITIES AND NEEDS -- 6 The Research Support System -- HUMAN RESOURCES -- Colleges and Graduate Schools -- Undergraduate Education -- Graduate Education -- Postdoctoral Training and Collaboration -- Postdoctoral Fellowships and Traineeships -- Advanced Training Institutes -- Collaboration and Communication -- TECHNOLOGICAL RESOURCES -- Computers -- Neuroimaging Devices -- Animal Care -- DATA RESOURCES -- Large-Scale Data Bases -- Research Access to Government Data -- Corporate and Local Government Archives -- FUNDING RESOURCES -- Modes of Support -- Grant Size and Duration -- The Disciplines and Interdisciplinary Research -- Interdisciplinary Research Centers -- THE PROBLEM OF VOICE -- 7 Raising the Scientific Yield -- RESEARCH FRONTIERS -- Behavior, Mind, and Brain -- Motivational and Social Contexts of Behavior -- Choice and Allocation -- Institutions and Cultures -- Methods of Data Collection, Representation, and Analysis -- RECOMMENDED NEW RESOURCES -- Human Resources -- Technological Resources -- Data Resources -- Interdisciplinary Research Centers -- Investigator-Initiated Grants -- Research Agency Changes -- CONCLUSION -- Appendix A Trends in Support for Research in the Behavioral and Social Sciences -- FEDERAL SUPPORT…”

Tabla de Contenidos: “…-- Chapter 2: Mathematics and Statistical Methods -- Chapter 3: Data Collection and Storage -- Chapter 4: Data Exploration and Analysis -- Chapter 5: Data Processing and Preparation -- Chapter 6: Modeling and Evaluation -- Chapter 7: Model Validation and Deployment -- Chapter 8: Unsupervised Machine Learning…”

Tabla de Contenidos: “…Spotted Arrays 9 -- 1.6 Summary 11 -- 1.7 Further Reading 13 -- 2 Overview of Data Analysis 15 -- 3 Basic Data Analysis 17 -- 3.1 Absolute Measurements 17 -- 3.2 Scaling 18 -- 3.2.1 Example: Linear and Nonlinear Scaling 20 -- 3.3 Detection of Outliers 20 -- 3.4 Fold Change 21 -- 3.5 Significance 22 -- 3.5.1 Nonparametric Tests 24 -- 3.5.2 Correction for Multiple Testing 24 -- 3.5.3 Example I: t-Test and ANOVA 25 -- 3.5.4 Example II: Number of Replicates 26 -- 3.6 Summary 28 -- 3.7 Further Reading 29 -- 4 Visualization by Reduction of Dimensionality 33 -- 4.1 Principal Component Analysis 33 -- 4.2 Example 1: PCA on Small Data Matrix 35 -- 4.3 Example 2: PCA on Real Data 37 -- 4.4 Summary 37 -- 4.5 Further Reading 39 -- 5 Cluster Analysis 41 -- 5.1 Hierarchical Clustering 41 -- 5.2 K-means Clustering 43 -- 5.3 Self-Organizing Maps 44 -- 5.4 Distance Measures 45 -- 5.4.1 Example: Comparison of Distance Measures 47 -- 5.5 Normalization 49 -- 5.6 Visualization of Clusters 50 -- 5.6.1 Example: Visualization of Gene Clusters in -- Bladder Cancer 50 -- 5.7 Summary 50 -- 5.8 Further Reading 52 -- 6 Beyond Cluster Analysis 55 -- 6.1 Function Prediction 55 -- 6.2 Discovery of Regulatory Elements in Promoter -- Regions 56 -- 6.2.1 Example 1: Discovery of Proteasomal Element 57 -- 6.2.2 Example 2: Rediscovery of Mlu Cell Cycle -- Box (MCB) 57 -- 6.3 Integration of data 58 -- 6.4 Summary 59 -- 6.5 Further Reading 59 -- 7 Reverse Engineering of Regulatory Networks 63 -- 7.1 The Time-Series Approach 63 -- 7.2 The Steady-State Approach 64 -- 7.3 Limitations of Network Modeling 65 -- 7.4 Example 1: Steady-State Model 65 -- 7.5 Example 2: Steady-State Model on Real Data 66 -- 7.6 Example 3: Steady-State Model on Real Data 68 -- 7.7 Example 4: Linear Time-Series Model 68 -- 7.8 Further Reading 71 -- 8 Molecular Classifiers 75 -- 8.1 Classification Schemes 76 -- 8.1.1 Nearest Neighbor 76 -- 8.1.2 Neural Networks 76 -- 8.1.3 Support Vector Machine 76 -- 8.2 Example I: Classification of Cancer Subtypes 77 -- 8.3 Example II: Classification of Cancer Subtypes 78 -- 8.4 Summary 79 -- 8.5 Further Reading 79 -- 9 Selection of Genes for Spotting on Arrays 81 -- 9.1 Gene Finding 82 -- 9.2 Selection of Regions Within Genes 82 -- 9.3 Selection of Primers for PCR 83 -- 9.4 Selection of Unique Oligomer Probes 83 -- 9.4.1 Example: Finding PCR Primers for Gene -- AF105374 83 -- 9.5 Experimental Design 84 -- 9.6 Further Reading 84 -- 10 Limitations of Expression Analysis 87 -- 10.1 Relative VersusAbsoluteRNA Quantification 88 -- 10.2 Further Reading 88 -- 11 Genotyping Chips 91 -- 11.1 Example: NeuralNetworksfor GeneChipprediction 91 -- 11.2 Further Reading 93 -- 12 Software Issues and Data Formats 95 -- 12.1 Standardization Efforts 96 -- 12.2 Standard File Format 97 -- 12.2.1 Example: Small Scripts in Awk 97 -- 12.3 Software for Clustering 98 -- 12.3.1 Example: Clustering with ClustArray 99 -- 12.4 Software for Statistical Analysis 99 -- 12.4.1 Example: StatisticalAnalysis with R 99 -- 12.4.2 The affyR Software Package 103 -- 12.4.3 Commercial Statistics Packages 103 -- 12.5 Summary 103 -- 12.6 Further Reading 104 -- 13 Commercial Software Packages 105 -- 14 Bibliography 109 -- Index 123.…”
Descripción del editor
Índice
Información biográfica

Tabla de Contenidos: “…. -- Chapter 3: Analyzing Data Statistically -- Introduction -- Generating descriptive statistics -- How to do it... -- Generating summary statistics -- How to do it... -- Generating summary statistics from multiple distributions -- How to do it... -- There's more...…”

Tabla de Contenidos: “…. -- See also -- Exploring descriptive statistics -- Getting ready -- How to do it... -- How it works... -- There's more... -- Descriptive statistics for aggregated columns -- See also -- Computing correlations -- Getting ready -- How to do it... -- How it works... -- There's more... -- Drawing histograms -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Visualizing interactions between features -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 5: Machine Learning with MLlib -- Loading the data -- Getting ready -- How to do it... -- How it works... -- There's more... -- Exploring the data -- Getting ready -- How to do it... -- How it works... -- Numerical features -- Categorical features -- There's more... -- See also -- Testing the data -- Getting ready -- How to do it... -- How it works... -- See also... -- Transforming the data -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also... -- Standardizing the data -- Getting ready -- How to do it... -- How it works... -- Creating an RDD for training -- Getting ready -- How to do it... -- Classification -- Regression -- How it works... -- There's more... -- See also…”

Tabla de Contenidos: “…Considering Timing and Performance -- Benchmarking with timeit -- Working with the memory profiler -- Running in Parallel on Multiple Cores -- Performing multicore parallelism -- Demonstrating multiprocessing -- Chapter 13 Exploring Data Analysis -- The EDA Approach -- Defining Descriptive Statistics for Numeric Data -- Measuring central tendency -- Measuring variance and range -- Working with percentiles -- Defining measures of normality -- Counting for Categorical Data -- Understanding frequencies -- Creating contingency tables -- Creating Applied Visualization for EDA -- Inspecting boxplots -- Performing t-tests after boxplots -- Observing parallel coordinates -- Graphing distributions -- Plotting scatterplots -- Understanding Correlation -- Using covariance and correlation -- Using nonparametric correlation -- Considering the chi-square test for tables -- Modifying Data Distributions -- Using different statistical distributions -- Creating a Z-score standardization -- Transforming other notable distributions -- Chapter 14 Reducing Dimensionality -- Understanding SVD -- Looking for dimensionality reduction -- Using SVD to measure the invisible -- Performing Factor Analysis and PCA -- Considering the psychometric model -- Looking for hidden factors -- Using components, not factors -- Achieving dimensionality reduction -- Squeezing information with t-SNE -- Understanding Some Applications -- Recognizing faces with PCA -- Extracting topics with NMF -- Recommending movies -- Chapter 15 Clustering -- Clustering with K-means -- Understanding centroid-based algorithms -- Creating an example with image data -- Looking for optimal solutions -- Clustering big data -- Performing Hierarchical Clustering -- Using a hierarchical cluster solution -- Using a two-phase clustering solution -- Discovering New Groups with DBScan…”

Tabla de Contenidos: “…TFO Blacklist Operation -- Directed Mode -- Adaptive and Static TCP Buffering -- Replication Acceleration -- Application Traffic Policy -- Application Groups -- Traffic Classifiers -- Policy Maps -- Negotiating Policies -- EndPoint Mapper Classification -- Monitoring and Reporting -- Automatic Discovery Statistics -- Connection Statistics and Details -- WAN Optimization Statistics -- Network Profiling -- Understanding WAAS Performance Improvement -- Understanding Device and System Performance and Scalability Metrics -- Executive Reports -- Integration with Third-Party Visibility Systems -- WAN Optimization Monitoring with XML-API -- Application Response Time Monitoring -- Summary -- Chapter 9 Configuring Application Acceleration -- Application Acceleration Overview -- CIFS Acceleration -- Windows Print Acceleration -- NFS Acceleration -- MAPI Acceleration -- HTTP Acceleration -- SSL Acceleration -- Video Acceleration -- Enabling Acceleration Features -- Additional Video Settings -- Configuring SSL Acceleration -- Configuring Disk Encryption -- Managing the Secure Store -- Configuring SSL Accelerated Services -- Using the CM GUI to Configure SSL -- Using the CLI to Configure SSL -- Configuring Preposition -- Acceleration Monitoring and Reporting -- Acceleration Monitoring Using Device CLI -- Acceleration Monitoring Using CM GUI -- Acceleration Monitoring with XML-API -- CIFSStats -- SSLStats -- VideoStats -- HttpStats -- MapiStats -- NfsStats -- Summary -- Chapter 10 Branch Office Virtualization -- Branch Office Virtualization Overview -- Overview of Virtual Blades -- Management of Virtual Blades -- Virtual Blade Hardware Emulation -- Virtualization Capable WAAS Platforms -- Creating Virtual Blades -- Guest OS Boot Image -- Configuring Virtual Blade Resources -- Virtual Blade Interface Bridging Considerations -- Starting Virtual Blades…”

Tabla de Contenidos: “…Classification of OECD National Accounts and Revenue Statistics -- Table 5. Tax ratios: Mendoza et al. (1994) versus other studies -- 4.1. …”

Tabla de Contenidos: “…/ Richard Naud and Wolfram Gerstner -- Statistical identification of synchronous spiking / Matthew T. …”
Enlace del recurso

Tabla de Contenidos: “…Chapter 8: Running Descriptive Statistics in AccessChapter 9: Scheduling and Running Batch Analysis; Chapter 10: Leveraging VBA to Enhance Data Analysis; Part IV: Reports, Dashboards, and Visualization in Access; Chapter 11: Presenting Data with Access Reports; Chapter 12: Using Pivot Tables and Pivot Charts in Access; Chapter 13: Enhancing Queries and Reports with Visualizations; Part V: Advanced Excel and Access Integration Techniques; Chapter 14: Getting Access Data into Excel; Chapter 15: Using VBA to Move Data between Excel and Access; Chapter 16: Exploring Excel and Access Automation…”

Tabla de Contenidos: “…Booth -- Single-digit arithmetic processing--anatomical evidence from statistical voxel-based lesion analysis / Urszula Mihulowicz, Klaus Willmes, Hans-Otto Karnath and Elise Klein -- Young children's use of derived fact strategies for addition and subtraction / Ann Dowker -- Decimal fraction representations are not distinct from natural number representations - evidence from a combined eye-tracking and computational modeling approach / Stefan Huber, Elise Klein, Klaus Willmes, Hans-Christoph Nuerk and Korbinian Moeller -- Optimized gamma synchronization enhances functional binding of frontoparietal cortices in mathematically gifted adolescents during deductive reasoning / Li Zhang, John Q. …”

Tabla de Contenidos: “…-- Local, General and Universal Prediction Methods: A Game-Theoretical Approach to the Problem of Induction -- Multiple Contraction Revisited -- Statistical Inference Without Frequentist Justifications -- Carnap and the Perils of Ramseyfication -- Naturalizing Meaning Through Epistemology: Some Critical Notes -- What Games Do Scientists Play? …”

Tabla de Contenidos: “…Early interactions of quantum statistics and quantum mechanics / Daniela Monaldi. …”