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Penetration models – use test market data to develop acceptance equations of expected sales volume as a function of time. Three examples of penetration models are: Bass trial only model; Bass declining trial model; Fourt and Woodlock model; Trial/Repeat models – number of repeat buyers is a function of the number of trial buyers.
The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]
The rapid, recent (as of early 2007) growth in online social networks (and other virtual communities) has led to an increased use of the Bass diffusion model. The Bass diffusion model is used to estimate the size and growth rate of these social networks. The work by Christian Bauckhage and co-authors [10] shows that the Bass model provides a ...
The diffusion of an innovation typically follows an S-shaped curve which often resembles a logistic function. Roger's diffusion model concludes that the popularity of a new product will grow with time to a saturation level and then decline, but it cannot predict how much time it will take and what the saturation level will be.
A diffusion line (also known as a bridge line) [1] is a secondary line of merchandise created by a high-end fashion house or fashion designer that retails at lower prices. [2] These ranges are separate from a fashion house's "signature line", or principal artistic line, that typically retails at much higher prices.
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Diffusion lines are an offshoot of a company or a designer's original line that is less expensive in order to reach a broader market and gain a wider consumer base. Another strategy used in massification is brand extensions, which is when an already established company releases a new product under their name.
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [ 3 ] Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian ) on training images.