Products related to Gaussian:
-
Gaussian Processes for Machine Learning
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines.GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning.The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms.A wide variety of covariance (kernel) functions are presented and their properties discussed.Model selection is discussed both from a Bayesian and a classical perspective.Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others.Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed.The book contains illustrative examples and exercises, and code and datasets are available on the Web.Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Price: 48.00 £ | Shipping*: 0.00 £ -
Gaussian Integrals and their Applications
Gaussian Integrals form an integral part of many subfields of applied mathematics and physics, especially in topics such as probability theory, statistics, statistical mechanics, quantum mechanics and so on.They are essential in computing quantities such as the statistical properties of normal random variables, solving partial differential equations involving diffusion processes, and gaining insight into the properties of particles.In Gaussian Integrals and their Applications, the author has condensed the material deemed essential for undergraduate and graduate students of physics and mathematics, such that for those who are very keen would know what to look for next if their appetite for knowledge remains unsatisfied by the time they finish reading this book. FeaturesA concise and easily digestible treatment of the essentials of Gaussian IntegralsSuitable for advanced undergraduates and graduate students in mathematics, physics, and statisticsThe only prerequisites are a strong understanding of multivariable calculus and linear algebra. Supplemented by numerous exercises (with fully worked solutions) at the end, which pertain to various levels of difficulty and are inspired by different fields in which Gaussian integrals are used.
Price: 48.99 £ | Shipping*: 0.00 £ -
Statistical Modeling Using Local Gaussian Approximation
Statistical Modeling using Local Gaussian Approximation extends powerful characteristics of the Gaussian distribution, perhaps, the most well-known and most used distribution in statistics, to a large class of non-Gaussian and nonlinear situations through local approximation.This extension enables the reader to follow new methods in assessing dependence and conditional dependence, in estimating probability and spectral density functions, and in discrimination.Chapters in this release cover Parametric, nonparametric, locally parametric, Dependence, Local Gaussian correlation and dependence, Local Gaussian correlation and the copula, Applications in finance, and more. Additional chapters explores Measuring dependence and testing for independence, Time series dependence and spectral analysis, Multivariate density estimation, Conditional density estimation, The local Gaussian partial correlation, Regression and conditional regression quantiles, and a A local Gaussian Fisher discriminant.
Price: 97.99 £ | Shipping*: 0.00 £ -
Yura - Gaussian Unopened Cd For Sale
🌟 Welcome to Stargoodskorea’s shop! 📝 Please read and follow our guidelines for a smooth shopping experience. Distributor 🌟 Authentic K-POP Merchandise: Sourced directly from South Korea, offering 100% original K-pop albums and a wide variety of star-related goods. Notice ✔️ 100% authentic products directly from South Korea.(if not, we guarantee a 100% refund.) ✔️ Product components may vary slightly post-release. ✔️ Please record an unboxing video to support any claims. ✔️ Minor scratches or color variations are not grounds for exchange or return. 🌐 Stargoodskorea is a Korea-based global retailer specializing in authentic, factory-sealed K-pop albums and merchandise for fans worldwide. Keywords #Yura,#-,#GAUSSIAN,#unopened,#CD,#for,#sale,#stargoods,#stargoodskorea,#kstar,
Price: 106.28 € | Shipping*: 0.0 €
-
What is a Gaussian distribution?
A Gaussian distribution, also known as a normal distribution, is a type of probability distribution that is symmetric and bell-shaped. It is characterized by its mean (average) and standard deviation, which determine the center and spread of the distribution, respectively. In a Gaussian distribution, the majority of the data points cluster around the mean, with fewer data points further away from the mean in a predictable pattern. Many natural phenomena and human characteristics follow a Gaussian distribution, making it a widely used concept in statistics and data analysis.
-
What is the Gaussian elimination method?
Gaussian elimination is a method used in linear algebra to solve systems of linear equations. It involves transforming the system of equations into row-echelon form by performing a series of row operations, such as adding multiples of one row to another or multiplying a row by a constant. Once the system is in row-echelon form, it becomes easier to solve for the variables using back substitution. This method is widely used in various fields such as engineering, physics, and computer science for solving complex systems of equations.
-
Can you explain the Gaussian algorithm?
The Gaussian algorithm, also known as Gaussian elimination, is a method used to solve systems of linear equations by transforming the augmented matrix into row-echelon form and then into reduced row-echelon form. This process involves using elementary row operations such as adding or subtracting rows, multiplying a row by a non-zero constant, and swapping rows. By performing these operations, the algorithm simplifies the system of equations and allows for the solution to be easily determined. The end result is a diagonal or triangular matrix that represents the solution to the system of equations.
-
What are the solutions for the Gaussian algorithm?
The Gaussian algorithm, also known as Gaussian elimination, is a method for solving systems of linear equations. The solutions for the Gaussian algorithm are the values of the variables that satisfy all the equations in the system. These solutions can be found by performing row operations on the augmented matrix of the system until it is in row-echelon form, and then solving for the variables using back substitution. If the system is consistent and has a unique solution, the Gaussian algorithm will find that solution. If the system is inconsistent or has infinitely many solutions, the Gaussian algorithm will indicate that as well.
Similar search terms for Gaussian:
-
Yura Gaussian Unopened Cd For Sale
🌟 Welcome to Stargoodskorea’s shop! 📝 Please read and follow our guidelines for a smooth shopping experience. Distributor 🌟 Authentic K-POP Merchandise: Sourced directly from South Korea, offering 100% original K-pop albums and a wide variety of star-related goods. Notice ✔️ 100% authentic products directly from South Korea.(if not, we guarantee a 100% refund.) ✔️ Product components may vary slightly post-release. ✔️ Please record an unboxing video to support any claims. ✔️ Minor scratches or color variations are not grounds for exchange or return. 🌐 Stargoodskorea is a Korea-based global retailer specializing in authentic, factory-sealed K-pop albums and merchandise for fans worldwide. Keywords #Yura,#GAUSSIAN,#unopened,#CD,#for,#sale,#stargoods,#stargoodskorea,#kstar,
Price: 99.99 € | Shipping*: 0.0 € -
Nonlinear Valuation and Non-Gaussian Risks in Finance
What happens to risk as the economic horizon goes to zero and risk is seen as an exposure to a change in state that may occur instantaneously at any time?All activities that have been undertaken statically at a fixed finite horizon can now be reconsidered dynamically at a zero time horizon, with arrival rates at the core of the modeling.This book, aimed at practitioners and researchers in financial risk, delivers the theoretical framework and various applications of the newly established dynamic conic finance theory.The result is a nonlinear non-Gaussian valuation framework for risk management in finance.Risk-free assets disappear and low risk portfolios must pay for their risk reduction with negative expected returns.Hedges may be constructed to enhance value by exploiting risk interactions.Dynamic trading mechanisms are synthesized by machine learning algorithms.Optimal exposures are designed for option positioning simultaneously across all strikes and maturities.
Price: 99.99 £ | Shipping*: 0.00 £ -
Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
This book introduces Bayesian reasoning and Gaussian processes into machine learning applications.Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery.It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets.Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework.The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studiesThis book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.
Price: 110.00 £ | Shipping*: 0.00 £ -
Handbook for Applied Modeling: Non-Gaussian and Correlated Data
Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data.Many practitioners work with data that fail the assumptions of the common linear regression models, necessitating more advanced modeling techniques.This Handbook presents clearly explained modeling options for such situations, along with extensive example data analyses.The book explains core models such as logistic regression, count regression, longitudinal regression, survival analysis, and structural equation modelling without relying on mathematical derivations.All data analyses are performed on real and publicly available data sets, which are revisited multiple times to show differing results using various modeling options.Common pitfalls, data issues, and interpretation of model results are also addressed.Programs in both R and SAS are made available for all results presented in the text so that readers can emulate and adapt analyses for their own data analysis needs.Data, R, and SAS scripts can be found online at http://www.spesi.org.
Price: 36.99 £ | Shipping*: 0.00 £
-
How do I calculate the Gaussian normal distribution?
To calculate the Gaussian normal distribution, you need to know the mean (μ) and standard deviation (σ) of the data set. The formula for calculating the Gaussian normal distribution is: f(x) = (1/(σ√(2π))) * e^(-(x-μ)^2/(2σ^2)), where e is the base of the natural logarithm. You can plug in the values of x, μ, and σ into this formula to calculate the probability density function at a specific point x. This formula helps in understanding the distribution of data points around the mean in a bell-shaped curve.
-
Can someone quickly explain the Gaussian sum formula?
The Gaussian sum formula, also known as the sum of the first n natural numbers, is given by the formula: n(n+1)/2. This formula allows us to quickly calculate the sum of the first n natural numbers without having to manually add them up. For example, if we want to find the sum of the first 10 natural numbers, we can simply plug in n=10 into the formula to get 10(10+1)/2 = 55. This formula is derived from a pattern in the sum of consecutive natural numbers and is widely used in mathematics and computer science.
-
How do you generate zeros in Gaussian elimination?
Zeros are generated in Gaussian elimination by performing row operations to eliminate variables in the system of linear equations. These row operations include multiplying a row by a non-zero constant, adding or subtracting a multiple of one row from another, and swapping rows. By carefully applying these operations, zeros can be generated in the matrix representation of the system, ultimately leading to a row-echelon form or reduced row-echelon form where the system can be easily solved.
-
Can someone help me with the Gaussian elimination method?
Yes, the Gaussian elimination method is a technique used to solve systems of linear equations by transforming the augmented matrix into row-echelon form and then back-substituting to find the solution. To perform Gaussian elimination, you start by writing the augmented matrix of the system, then use row operations to transform the matrix into row-echelon form. Once the matrix is in row-echelon form, you can use back-substitution to find the solution to the system of equations. If you need help with the specific steps and calculations involved in Gaussian elimination, it may be helpful to seek out a tutor or online resources that provide detailed explanations and examples.
* All prices are inclusive of VAT and, if applicable, plus shipping costs. The offer information is based on the details provided by the respective shop and is updated through automated processes. Real-time updates do not occur, so deviations can occur in individual cases.