Estimation of Entropy and Mutual Information
... entropy function to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators. The setup is related to Grenander ’s method of sieves and places no assumptions on the underlying probability measure generating the data. Second, we p ...
... entropy function to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators. The setup is related to Grenander ’s method of sieves and places no assumptions on the underlying probability measure generating the data. Second, we p ...
Statistical Techniques for Language Recognition
... paper explains how to recognize language automatically with statistical techniques. Recognizing \valid" plaintext is a crucial step in many cryptanalytic tasks. For example, this step can enable a cryptanalyst to carry out an exhaustive key search of any cryptosystem, given ciphertext only: For each ...
... paper explains how to recognize language automatically with statistical techniques. Recognizing \valid" plaintext is a crucial step in many cryptanalytic tasks. For example, this step can enable a cryptanalyst to carry out an exhaustive key search of any cryptosystem, given ciphertext only: For each ...
Chapter 4 Probability
... called a probability measure, on the sample space. Probability measures satisfy certain axioms (to be introduced later) and have special mathematical properties, so not just any mathematical function will do. But in any given physical circumstance there are typically all sorts of probability measure ...
... called a probability measure, on the sample space. Probability measures satisfy certain axioms (to be introduced later) and have special mathematical properties, so not just any mathematical function will do. But in any given physical circumstance there are typically all sorts of probability measure ...
Bayesian Nonparametric Models Definition Peter Orbanz, Cambridge University
... not homogeneous, in fact they are often structured in interesting ways. For example, the data generating process might change over time thus observations at different times are not exchangeable, or observations might come in distinct groups with those in the same group being more similar than across ...
... not homogeneous, in fact they are often structured in interesting ways. For example, the data generating process might change over time thus observations at different times are not exchangeable, or observations might come in distinct groups with those in the same group being more similar than across ...
Extreme Value Statistics of Eigenvalues of Gaussian Random Matrices
... While the semi-circle law provides a global information about how the eigenvalues are typically distributed, unfortunately it does not contain enough information about the probabilities of rare events. The questions concerning rare events have recently come up in different contexts. For example, str ...
... While the semi-circle law provides a global information about how the eigenvalues are typically distributed, unfortunately it does not contain enough information about the probabilities of rare events. The questions concerning rare events have recently come up in different contexts. For example, str ...
Statistics
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or societal problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as ""all persons living in a country"" or ""every atom composing a crystal"". Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methodologies are used in data analysis: descriptive statistics, which summarizes data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draws conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and a synthetic data drawn from idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a ""false positive"") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a ""false negative""). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other important types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data and/or censoring may result in biased estimates and specific techniques have been developed to address these problems.Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. Statistics continues to be an area of active research, for example on the problem of how to analyze Big data.