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. Given an observation x = (x₁, …, x_d) from a multinomial distribution with N trials and parameter vector θ = (θ₁, …, θ_d), a "smoothed" version of the data gives the estimator:

${\hat {\theta }}_{i}={\frac {x_{i}+\alpha }{N+\alpha d}}\qquad (i=1,\ldots ,d),$

where α > 0 is the smoothing parameter (α = 0 corresponds to no smoothing). Additive smoothing is a type of shrinkage estimator, as the resulting estimate will be between the empirical estimate x_i/n, and the uniform probability 1/d. Using Laplace's rule of succession, some authors have argued that α should be 1 (in which case the term add-one smoothing is also used), though in practice a smaller value is typically chosen.

From a Bayesian point of view, this corresponds to the expected value of the posterior distribution, using a symmetric Dirichlet distribution with parameter α as a prior.

History

According to Andrew Ng, Laplace came out with this smoothing technique when he tried to estimate the chance that Sun will rise tomorrow. His rational was that even given a large sample of days with rising Sun, we still can not be completely sure that Sun will rise also tomorrow.

Applications

Classification

Additive smoothing is commonly a component of naive Bayes classifiers.

Statistical language modelling

In a bag of words model of natural language processing and information retrieval, the data consists of the number of occurrences of each word in a document. Additive smoothing allows the assignment of non-zero probabilities to words which do not occur in the sample.

Chen & Goodman (1996) empirically compare additive smoothing to a variety of other techniques, using both α fixed at one and a more general value.

See also

• Bayesian average
• Pseudocount

External links

• SF Chen, J Goodman (1996). "An empirical study of smoothing techniques for language modeling". Proceedings of the 34th annual meeting on Association for Computational Linguistics.