One parameter was changed for the Willshaw model, while the Koulakov model required us to rebalance the values of the neural activity and the chemical cue strength.
Our model requires us to be innovative about training and support so we are structured differently from other pathways to teaching.
"Designing a car for this business model requires us to sell performance, not just cars," Spowers says.
However, note that writing down a model requires us to specify a different probability measure for each partition cell which can be quite cumbersome.
Third, the model requires us to take into account the temporal dimension: how the actors, processes and impact of effectors change over both chronological and life course time.
This hypothesis was also accommodated by one set of models, whereby it required us to alter the value for c2, when no surround stimulus was present as the narrow focus of attention and the variable focus of attention models would otherwise be identical.
However, SNB tended to fail to converge in a number of instances, making it an unreliable model in practice and required us to have a 'replacement' in our decision rule.
The fact that the intensity-dependent effects are functions of terms to be estimated by the model (i.e. BX + AZ) required us to develop a novel algorithm.
The principle of parsimony was used, in other words we do not reject the complex (saturated) model (e.g., ACE) until evidence in support of a more simplex model (e.g., AE) requires us to abandon it.
To model the distribution of the hidden states, we have applied the Hierarchical Dirichlet Process Hidden Markov Model, a non-parametric extension of the traditional Hidden Markov Model, which does not require us to fix the number of hidden states in advance.
