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A writing implement or writing instrument is an object used to produce writing. Most of these items can be also used for other functions such as painting, drawing and technical drawing, but writing instruments generally have the ordinary requirement to create a smooth, controllable line.
Another writing implement employed by a smaller population, is the stylus used by blind users in conjunction with the slate for punching out the dots in Braille.
Société Bic (commonly referred to just as Bic) is a company based in Clichy, France. It was founded in 1945 by Baron Marcel Bich and has become known for making disposable consumer products such as lighters, magnets, ballpoint pens, shaving razors, printed paper products and watersports products. The brand's lighters have changed little since 1972. They, as well as the Bic Cristal ballpoint pen, are easily recognizable as a result of their importance in pop culture. As such, they are represented in the design collection of the Museum Of Modern Art in New York. The company competes in most markets against Faber-Castell, Global Gillette, Newell Rubbermaid and Schwan-Stabilo. The Bic pen, more correctly the Bic Cristal, was the company's first product.
Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be correct.
In statistics, the Bayesian information criterion (BIC) or Schwarz criterion (also SBC, SBIC) is a criterion for model selection among a finite set of models. It is based, in part, on the likelihood function and it is closely related to the Akaike information criterion (AIC).
When fitting models, it is possible to increase the likelihood by adding parameters, but doing so may result in overfitting. Both BIC and AIC resolve this problem by introducing a penalty term for the number of parameters in the model; the penalty term is larger in BIC than in AIC.