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For any given and a distribution over models, the observation density conditioned on the unit can be written as (5).

The Kalman filtering method is based on the use of two models: the observation model (for updating or correction) and the underlying dynamic model (for prediction).

Stochastic resonance uses noise to amplify weak impulses while HMM approach models the observation of a system as a probabilistic function of the system hidden states.

The concept of conditional HMMs [22] models the observation y n as depending on the previous observations at time shifts (boldsymbol {psi } = (psi _{1}, ldots, psi _{P}) in mathbb {N}^{P}).

Our approach explicitly models the observation layer, which includes a heavy-tailed Student-t likelihood, and allows for a hierarchy of multiple transition layers to learn the system dynamics directly from estimation data contaminated by outliers.

The conventional learning curve equation describes and models the observation that costs decrease by a constant percentage every time the quantity doubles.

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We model the observation process by assuming that a mutation i is falsely observed with probability ϵ as in Equation (2).

In this section the on-road and off-road motion models and the observation model are described in more detail compared to the introduction in Section 2.1.

To test the model, the observation is plotted versus the model prediction (Fig. 8).

Color histograms and EOHs are selected as visual features to model the observation of vehicles and integrated by a democratic integration strategy, and the observation model is embedded in a PF tracking framework.

By maximizing the conditional probability p(Y∣ X) from the training dataset, conditional models do not explicitly model the observation sequences.

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