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Bayesian Structural Time Series
Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data. The model has also promising application in the field of analytical marketing. In particular, it can be used in order to assess how much different marketing campaigns have contributed to the change in web search volumes, product sales, brand popularity and other relevant indicators. Difference-in-differences models and interrupted time series designs are alternatives to this approach. "In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical
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 expe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ensemble Learning
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives. Overview Supervised learning algorithms perform the task of searching through a hypothesis space to find a suitable hypothesis that will make good predictions with a particular problem. Even if the hypothesis space contains hypotheses that are very well-suited for a particular problem, it may be very difficult to find a good one. Ensembles combine multiple hypotheses to form a (hopefully) better hypothesis. The term ''ensemble'' is usually reserved for methods that generate multiple hypotheses using the same base lear ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making pred ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jennifer A
Jennifer or Jenifer may refer to: People *Jennifer (given name) * Jenifer (singer), French pop singer * Jennifer Warnes, American singer who formerly used the stage name Jennifer * Daniel of St. Thomas Jenifer * Daniel Jenifer Film and television * ''Jennifer'' (1953 film), a film starring Ida Lupino * ''Jennifer'' (1978 film), a horror film by Brice Mack * ''Jennifer'', a 1998 Ghanaian film starring Brew Riverson Jnr * "Jenifer" (''Masters of Horror''), an episode of ''Masters of Horror'' Music * The Jennifers, a British band, some of whose members later formed Supergrass * ''Jenifer'' (album), an album by French singer Jenifer * ''Jennifer'' (album), a 1972 album by Jennifer Warnes * "Jennifer", a 1974 song by Faust from ''Faust IV'' * "Jennifer", a 1983 song by Eurythmics from ''Sweet Dreams (Are Made of This)'' (album) * "Jennifer", a 2001 song by M2M from ''The Big Room'' Other uses * Hurricane Jennifer * Project Jennifer, a CIA attempt to recover a Soviet s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spike-and-slab Regression
Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique that is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005). Model description Suppose we have ''P'' possible predictors in some model. Vector ''γ'' has a length equal to ''P'' and consists of zeros and ones. This vector indicates whether a particular variable is included in the regression or not. If no specific prior information on initial inclusion probabilities of particular variables is available, a Bernoulli prior distri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Correlation Does Not Imply Causation
The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase ''cum hoc ergo propter hoc'' ('with this, therefore because of this'). This differs from the fallacy known as ''post hoc ergo propter hoc'' ("after this, therefore because of this"), in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one. As with any logical fallacy, identifying that the reasoning behind an argument is flawed does not necessarily imply that the resulting co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Inference Using Gibbs Sampling
Bayesian inference using Gibbs sampling (BUGS) is a statistical software for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods. It was developed by David Spiegelhalter at the Medical Research Council Biostatistics Unit in Cambridge in 1989 and released as free software in 1991. The BUGS project has evolved through four main versions: ClassicBUGS, WinBUGS, OpenBUGS anMultiBUGS MultiBUGS is built on the existing algorithms and tools in OpenBUGS and WinBUGS, which are no longer developed, and implements parallelization to speed up computation. Several R packages are availableR2MultiBUGSacts as an interface to MultiBUGS, whilNimbleis an extension of the BUGS language. Alternative implementations of the BUGS language include JAGS and Stan. See also * Spike and slab variable selection * Bayesian structural time series Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcast ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
R (programming Language)
R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language. According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages used in data mining. R ranks 12th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020. The official R software environment is an open-source free software environment within the GNU package, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for various operating systems. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spike-and-slab Variable Selection
Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique that is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005). Model description Suppose we have ''P'' possible predictors in some model. Vector ''γ'' has a length equal to ''P'' and consists of zeros and ones. This vector indicates whether a particular variable is included in the regression or not. If no specific prior information on initial inclusion probabilities of particular variables is available, a Bernoulli prior distri ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Feature Selection
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: :* simplification of models to make them easier to interpret by researchers/users, :* shorter training times, :* to avoid the curse of dimensionality, :*improve data's compatibility with a learning model class, :*encode inherent symmetries present in the input space. The central premise when using a feature selection technique is that the data contains some features that are either ''redundant'' or ''irrelevant'', and can thus be removed without incurring much loss of information. ''Redundant'' and ''irrelevant'' are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated. Feature sele ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Kalman Filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory. This digital filter is sometimes termed the ''Stratonovich–Kalman–Bucy filter'' because it is a special case of a more general, nonlinear filter developed somewhat earlier by the Soviet mathematician Ruslan Stratonovich. In fact, some of the special case linear filter's equations appeared in papers by Stratonovich that were published before summer 1960, when Kalman met with Stratonovich during a conference in Moscow. Kalman filtering has numerous te ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Interrupted Time Series
Interrupted time series analysis (ITS), sometimes known as quasi-experimental time series analysis, is a method of statistical analysis involving tracking a long-term period before and after a point of intervention to assess the intervention's effects. The time series refers to the data over the period, while the interruption is the intervention, which is a controlled external influence or set of influences. Effects of the intervention are evaluated by changes in the level and slope of the time series and statistical significance of the intervention parameters. Interrupted time series design is the design of experiments based on the interrupted time series approach. The method is used in various areas of research, such as: *political science: impact of changes in laws on the behavior of people; (e.g., Effectiveness of sex offender registration policies in the United States) *economics: impact of changes in credit controls on borrowing behavior; *sociology: impact of experiments in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
