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For template-based modelling of unknown structures, several structure prediction server are available [29].
In contrast, GP approaches provide a framework for the probabilistic modelling of unknown functions rather than unknown distributions.
These were introduced by Ferguson (1973) and Antoniak (1974), and provide a framework for the probabilistic modelling of unknown probability distributions.
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The availability of more reliable sequence search tools therefore helps to increase the success rates of the widely used approach to model proteins of unknown structures from homologous template structures [8].
Said, S. E. & Dickey, D. A. Testing for unit roots in autoregressive-moving average models of unknown order.
Because of the complex dynamics of discrete event systems (DESs), it is very difficult to obtain a model of unknown DESs from given input and output data.
On such a basis we sketch a model of unknown functions impinging on intentional actions through a high level form of (MA) reinforcement learning.
A new kernel invariance algorithm (KIA) is introduced to determine both the significant model terms and estimate the unknown parameters in non-linear continuous-time differential equation models of unknown systems.
This work was done to establish the molecular basis and regulation of the formation pattern of cadherin/β-catenin-mediated adherens junctions, using an animal model of unknown gene sequence, the toad Rhinella arenarum.
In underdetermined conditions, i.e., cases of dealing with how best to instantaneously and rapidly construct data-consistent prime models of unknown (or well-studied) complex system from small-sized time series data, inference of unknown underlying network of interaction is more challenging.
Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order.
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