Appendix A Detection and estimation in additive Gaussian noise A.1 Gaussian random variables A.1.1 Scalar real Gaussian random variables A standard Gaussian random variable w takes values over the real line and

Source URL: web.stanford.edu

Accounting for Exchange-Rate Variability in Present-Value Models When the Discount Factor Is Near 1 By CHARLES ENGEL AND

Source URL: www.ssc.wisc.edu

• Stochastic processes
• Actuarial science
• Discounting
• Monetary policy
• Variance
• Random walk
• Instrumental variable

StatisticsLecture 2 Discrete Random Variable “The probable is what usually happens.” (Aristotle )

Source URL: www-stat.wharton.upenn.edu

Journal of Experimental Psychology: Learning, Memory, and Cognition 2013, Vol. 39, No. 5, 1473–1490 © 2013 American Psychological Association/$12.00 DOI: a0032397

Source URL: cocosci.berkeley.edu

• Randomness
• Design of experiments
• Hypothesis testing
• Statistical randomness
• Statistical theory
• Fair coin
• Random sequence
• Likelihood-ratio test
• Random variable
• Statistical hypothesis testing
• Statistics
• Type I and type II errors

Cumulative Probabilities for the Standard Normal (Z) Distribution Values in the table correspond to the area under the curve of a standard normal random variable for a value at or below z .

Source URL: www.sjsu.edu

Extremal Dependence Concepts

Source URL: sas.uwaterloo.ca

• Mathematical analysis
• Probability
• Statistics
• Independence
• Multivariate statistics
• Probability theory
• Actuarial science
• Probability distributions
• Copula
• Comonotonicity
• Joint probability distribution
• Random variable

Discussion on Brownian distance covariance by G. J. Szekely and M. L. Rizzo

Source URL: select.cs.cmu.edu

• Covariance and correlation
• Multivariate statistics
• Probability theory
• Hilbert space
• Independence
• Positive-definite kernel
• Distance correlation
• Reproducing kernel Hilbert space
• Covariance
• Characteristic function
• Kernel
• Multivariate random variable

Monte Carlo simulation in MS Excel The Monte Carlo method is based on the generation of multiple trials to determine the expected value of a random variable. The basis of the method is provided by the following relations

Source URL: cdn.projectsmart.co.uk

On the Relationship between Sum-Product Networks and Bayesian Networks Han Zhao HAN . ZHAO @ UWATERLOO . CA Mazen Melibari MMELIBAR @ UWATERLOO . CA

Source URL: jmlr.org

• Graphical models
• Statistical models
• Probability theory
• Bayesian network
• Networks
• Variable elimination
• Markov random field

Journal of Machine Learning Research1414 Submitted 5/02; Published 3/03 Ranking a Random Feature for Variable and Feature Selection

Source URL: www.jmlr.org