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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 statistics, the Bayesian information criterion (BIC) or Schwarz information criterion (also SIC, SBC, SBIC) is a criterion for model selection among a finite set of models; models with lower BIC are generally preferred.
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. [1] The sub-models combine to form the hierarchical model, and Bayes' theorem is used to integrate them with the observed data and account for all the ...
Dynamic Bayesian Network composed by 3 variables. Bayesian Network developed on 3 time steps. Simplified Dynamic Bayesian Network. All the variables do not need to be duplicated in the graphical model, but they are dynamic, too. A dynamic Bayesian network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time ...
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...
Bayesian model selection has also been used. Bayesian methods often quantify uncertainties of all sorts and answer questions hard to tackle by classical methods, such as what is the probability of having a change at a given time and what is the probability of the data having a certain number of changepoints. [8]
Bag-of-words model; Balanced clustering; Ball tree; Base rate; Bat algorithm; Baum–Welch algorithm; Bayesian hierarchical modeling; Bayesian interpretation of kernel regularization; Bayesian optimization; Bayesian structural time series; Bees algorithm; Behavioral clustering; Bernoulli scheme; Bias–variance tradeoff; Biclustering; BigML ...
When working with Bayesian models there are a series of related tasks that need to be addressed besides inference itself: Diagnoses of the quality of the inference, this is needed when using numerical methods such as Markov chain Monte Carlo techniques; Model criticism, including evaluations of both model assumptions and model predictions