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35 relations: Additive smoothing, Artificial neural network, Bag-of-words model, Bayesian average, Bayesian inference, Beta distribution, Binomial distribution, Conceptual model, Cromwell's rule, Density estimation, Dirichlet distribution, Estimator, Event (probability theory), Expected value, George James Lidstone, Halting problem, Hidden Markov model, Laplacian smoothing, Machine learning, Multinomial distribution, Naive Bayes classifier, Pierre-Simon Laplace, Posterior probability, Prediction by partial matching, Principle of indifference, Prior probability, Probability, Recommender system, Rule of succession, Sample (statistics), Shrinkage estimator, Smoothing, Statistics, Sunrise problem, 0.
Additive smoothing
In statistics, additive smoothing, also called Laplace smoothing (not to be confused with Laplacian smoothing), or Lidstone smoothing, is a technique used to smooth categorical data.
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Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Bag-of-words model
The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR).
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Bayesian average
A Bayesian average is a method of estimating the mean of a population using outside information, especially a pre-existing belief, that is factored into the calculation.
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Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
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Beta distribution
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval parametrized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.
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Binomial distribution
In probability theory and statistics, the binomial distribution with parameters n and p is the discrete probability distribution of the number of successes in a sequence of n independent experiments, each asking a yes–no question, and each with its own boolean-valued outcome: a random variable containing a single bit of information: success/yes/true/one (with probability p) or failure/no/false/zero (with probability q.
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Conceptual model
A conceptual model is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents.
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Cromwell's rule
Cromwell's rule, named by statistician Dennis Lindley, states that the use of prior probabilities of 0 ("the event will definitely not occur") or 1 ("the event will definitely occur") should be avoided, except when applied to statements that are logically true or false, such as 2+2 equaling 4 or 5.
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Density estimation
In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function.
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Dirichlet distribution
In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted \operatorname(\boldsymbol\alpha), is a family of continuous multivariate probability distributions parameterized by a vector \boldsymbol\alpha of positive reals.
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Estimator
In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.
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Event (probability theory)
In probability theory, an event is a set of outcomes of an experiment (a subset of the sample space) to which a probability is assigned.
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Expected value
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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George James Lidstone
George James Lidstone FIA FSA FRSE (1870-1952) was a British actuary who made several contributions to the field of statistics.
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Halting problem
In computability theory, the halting problem is the problem of determining, from a description of an arbitrary computer program and an input, whether the program will finish running (i.e., halt) or continue to run forever.
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Hidden Markov model
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states.
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Laplacian smoothing
Laplacian smoothing is an algorithm to smooth a polygonal mesh.
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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Multinomial distribution
In probability theory, the multinomial distribution is a generalization of the binomial distribution.
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Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
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Pierre-Simon Laplace
Pierre-Simon, marquis de Laplace (23 March 1749 – 5 March 1827) was a French scholar whose work was important to the development of mathematics, statistics, physics and astronomy.
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Posterior probability
In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account.
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Prediction by partial matching
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction.
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Principle of indifference
The principle of indifference (also called principle of insufficient reason) is a rule for assigning epistemic probabilities.
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Prior probability
In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account.
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Probability
Probability is the measure of the likelihood that an event will occur.
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Recommender system
A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.
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Rule of succession
In probability theory, the rule of succession is a formula introduced in the 18th century by Pierre-Simon Laplace in the course of treating the sunrise problem.
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Sample (statistics)
In statistics and quantitative research methodology, a data sample is a set of data collected and/or selected from a statistical population by a defined procedure.
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Shrinkage estimator
In statistics, a shrinkage estimator is an estimator that, either explicitly or implicitly, incorporates the effects of shrinkage.
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Smoothing
In statistics and image processing, to smooth a data set is to create an approximating function that attempts to capture important patterns in the data, while leaving out noise or other fine-scale structures/rapid phenomena.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Sunrise problem
The sunrise problem can be expressed as follows: "What is the probability that the sun will rise tomorrow?" The sunrise problem illustrates the difficulty of using probability theory when evaluating the plausibility of statements or beliefs.
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0
0 (zero) is both a number and the numerical digit used to represent that number in numerals.
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Redirects here:
Laplace Smoothing, Laplace smoothing, Lidstone smoothing, Pseudocount.
References
[1] https://en.wikipedia.org/wiki/Additive_smoothing
