Essay On Humour And Pathos In Poor Relation WORK
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Firstly, the learning task is much easier in the beginning because only a single chain is presented and the tumor cells and LNLs are in the healthy state. The transition probabilities for this starting state are already fixed in the learning process.
However, in the following situations, the new observed tumor cell should only be accepted into the probabilities if the model predicts it to be present. This is accomplished by the sensitivity vector, as parameterized in Eq. (11). E.g. for a tumor cell to be present:
The second problem is, that the transition matrix should be inferred in an iterative way: After presenting a tumor cell, the algorithm should predict for this cell the probability it will leave the healthy state. If the likelihood of this event is high and the cell has not yet been observed, then this cell must be accepted. However, if the cell has already been observed, then the algorithm must discount the likelihood and only accept a transition to the metastatic state only if the cell is predicted.
The algorithm reads the rules for each state separately (i.e. cell type → state ← disease state) and updates the state probabilities for each cell separately. For every observed tumor cell, the likelihood of the transition from the current state to the new state is determined and added to the state probability.
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