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Calibration (statistics)
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. "Calibration" can mean :*a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; :*procedures in statistical classification to determine class membership probabilities which assess the uncertainty of a given new observation belonging to each of the already established classes. In addition, "calibration" is used in statistics with the usual general meaning of calibration. For example, model calibration can be also used to refer to Bayesian inference about the value of a model's parameters, given some data set, or more generally to any type of fitting of a statistical model. As Philip Dawid puts it, "a forecaster is ''well calibrated'' if, for example, of those events to which he assign ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German: '' Statistik'', "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.Dodge, Y. (2006) ''The Oxford Dictionary of Statistical Terms'', Oxford University Press. 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 reasonably extend from the sample to the population as a whole. An ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Classification
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Superforecasting
''Superforecasting: The Art and Science of Prediction'' is a book by Philip E. Tetlock and Dan Gardner released in 2015. It details findings from The Good Judgment Project. Reviews ''The Economist'' reports that superforecasters are clever (with a good mental attitude), but not necessarily geniuses. It reports on the treasure trove of data coming from The Good Judgment Project, showing that accurately selected amateur forecasters (and the confidence they had in their forecasts) were often more accurately tuned than experts. According to ''The Wall Street Journal'', ''Superforecasting'' is "The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow." The ''Harvard Business Review'' paired it to the book ''How Not to Be Wrong, How Not to Be Wrong: The Power of Mathematical Thinking'' by Jordan Ellenberg. See also * Guesstimate * Fermi problem References External links Daily catch-up: dart-throwing chimpanzees and how to predict the future , ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Philip E
Philip, also Phillip, is a male given name, derived from the Greek (''Philippos'', lit. "horse-loving" or "fond of horses"), from a compound of (''philos'', "dear", "loved", "loving") and (''hippos'', "horse"). Prominent Philips who popularized the name include kings of Macedonia and one of the apostles of early Christianity. ''Philip'' has many alternative spellings. One derivation often used as a surname is Phillips. It was also found during ancient Greek times with two Ps as Philippides and Philippos. It has many diminutive (or even hypocoristic) forms including Phil, Philly, Lip, Pip, Pep or Peps. There are also feminine forms such as Philippine and Philippa. Antiquity Kings of Macedon * Philip I of Macedon * Philip II of Macedon, father of Alexander the Great * Philip III of Macedon, half-brother of Alexander the Great * Philip IV of Macedon * Philip V of Macedon New Testament * Philip the Apostle * Philip the Evangelist Others * Philippus of Croton (c. 6th c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Brier Score
The Brier Score is a ''strictly proper score function'' or ''strictly proper scoring rule'' that measures the accuracy of probabilistic predictions. For unidimensional predictions, it is strictly equivalent to the mean squared error as applied to predicted probabilities. The Brier score is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes or classes. The set of possible outcomes can be either binary or categorical in nature, and the probabilities assigned to this set of outcomes must sum to one (where each individual probability is in the range of 0 to 1). It was proposed by Glenn W. Brier in 1950. The Brier score can be thought of as a cost function. More precisely, across all items i\in in a set of ''N'' predictions, the Brier score measures the mean squared difference between: * The predicted probability assigned to the possible outcomes for item ''i'' * The actual outcome o_i Therefore, the ''lower'' the Bri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Forecasting
Forecasting is the process of making predictions based on past and present data. Later these can be compared (resolved) against what happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results. Prediction is a similar but more general term. Forecasting might refer to specific formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods or the process of prediction and resolution itself. Usage can vary between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period. Risk and uncertainty are central to forecasting and prediction; it is generally considered a good practice to indicate the degree of uncertainty ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Prediction
A prediction (Latin ''præ-'', "before," and ''dicere'', "to say"), or forecast, is a statement about a future event or data. They are often, but not always, based upon experience or knowledge. There is no universal agreement about the exact difference from " estimation"; different authors and disciplines ascribe different connotations. Future events are necessarily uncertain, so guaranteed accurate information about the future is impossible. Prediction can be useful to assist in making plans about possible developments. Opinion In a non-statistical sense, the term "prediction" is often used to refer to an informed guess or opinion. A prediction of this kind might be informed by a predicting person's abductive reasoning, inductive reasoning, deductive reasoning, and experience; and may be useful—if the predicting person is a knowledgeable person in the field. The Delphi method is a technique for eliciting such expert-judgement-based predictions in a controlled way ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sara Solla
Sara A. Solla is an Argentine-American physicist and neuroscientist whose research applies ideas from statistical mechanics to problems involving neural networks, machine learning, and neuroscience. She is a professor of physics and of physiology at Northwestern University. Education and career Solla is originally from Buenos Aires, and earned a licenciatura in physics in 1974 from the University of Buenos Aires. She completed a Ph.D. in physics in 1982 at the University of Washington. She became a postdoctoral researcher at Cornell University and at the Thomas J. Watson Research Center of IBM Research. Influenced to work in neural networks by a talk from John Hopfield at Cornell, she became a researcher in the neural networks group at Bell Labs. She took her present position at Northwestern University in 1997. Recognition Solla is a Member of the American Academy of Arts and Sciences (AAAS), and a Fellow of the American Physical Society The American Physical Society honors m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Peter Flach
Pieter Adriaan Flach (born 8 April 1961, Sneek) is a Dutch computer scientist and a Professor of Artificial Intelligence in the Department of Computer Science at the University of Bristol. He is author of the acclaimed Simply Logical: Intelligent Reasoning by Example (John Wiley, 1994) and Machine Learning: the Art and Science of Algorithms that Make Sense of Data (Cambridge University Press, 2012). Education Flach received an MSc Electrical Engineering from Universiteit Twente in 1987 and a in Computer Science from |
Logistic Regression
In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination). Formally, in binary logistic regression there is a single binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). The corresponding probability of the value labeled "1" can vary between 0 (certainly the value "0") and 1 (certainly the value "1"), hence the labeling; the function that converts log-odds to probability is the logistic function, hence the name. The unit of measurement for the log-odds ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Platt Scaling
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines, replacing an earlier method by Vapnik, but can be applied to other classification models. Platt scaling works by fitting a logistic regression model to a classifier's scores. Description Consider the problem of binary classification: for inputs , we want to determine whether they belong to one of two classes, arbitrarily labeled and . We assume that the classification problem will be solved by a real-valued function , by predicting a class label . For many problems, it is convenient to get a probability P(y=1, x), i.e. a classification that not only gives an answer, but also a degree of certainty about the answer. Some classification models do not provide such a probability, or give poor probability estimates. Platt scaling is an algo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Isotonic Regression
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Applications Isotonic regression has applications in statistical inference. For example, one might use it to fit an isotonic curve to the means of some set of experimental results when an increase in those means according to some particular ordering is expected. A benefit of isotonic regression is that it is not constrained by any functional form, such as the linearity imposed by linear regression, as long as the function is monotonic increasing. Another application is nonmetric multidimensional scaling, where a low-dimensional embedding for data points is sought such that order of distances between points in the embedding matches order of dissimilarity between points. Isotonic regression is used ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
