Generative models

Those based on strictly empirical descriptions Mathematical models based on physical and chemical laws (e.g., mass and energy balances, thermodynamics, chemical reaction kinefics) are frequently employed in optimization apphcations. These models are conceptually attractive because a gener model for any system size can be developed before the system is constructed. On the other hand, an empirical model can be devised that simply correlates input-output data without any physiochemical analysis of the process. For... 

Generative models can provide a high-resolution map for protein location. The availability of these maps should aid systems biology modeling of cell behavior. 

If the index function P(A(M) SC C)xN) of the generative model is sharp, as it is for carbon nucleosynthesis, then a well-defined posterior P(SC C)x A(M)N) is consistent with initial conditions only in the support of that index function. If A(M) is constructively posterior, and the constructive priors admitted counterfactuals relative to the... 

Rather than compute the probability that a token generates a tag P(/ t), an HMM is a generative model which computes P t l), that is, the probability of seeing a token given the tag. We can calculate one from the other by concatenating the HMM models to form sequences and then apply Bayes rule ... 

In robotics, this inverse probability is often coined generative model, since it describes, at some level of abstraction, how state variables X cause sensor measurements T [16]. 

One can obtain the labels and offsets of every term from the text. There are various statistical models that can be used in this process. Hidden Markov model (HMM) is the simplest of dynamic Bayesian model. HMM is a finite set of states, each of which is associated with a (generally mirltidimensional) probability distribution [41]. HMMs ate a form of generative models that define a joint probabihty... 

Skarka W (2007) Application of MOKA methodology in generative model creation using CATIA. Eng Appl Artif Intell 20 677-690... 

White D, Wilson RC (2008) Parts-based generative models for graphs. In 19th International Conference on Pattern Recognition (ICPR 2008), pp 1-4... 

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