BLOCKPOST Activation Code [License]
CLICK HERE ===== https://urlin.us/2tcZUe
First, the observation matrix is neither discrete ({W}{ég}{û}}, where by discrete I mean that the state of each agent can either be active or not but not that the state can be in one of a discrete set of states with probabilities) nor does it form a Markov chain ({W}{ég}{û}). As a consequence, the sum-product algorithm will not be suitable.
Second, the state space consists of two states which are not that clearly distinguished from each other. As a result, it would be difficult to determine how to define a change in the state of an agent. In addition, even if it is possible, it is not straightforward to appreciate the difference between these states: the "state of concept of normalization of an agent" and the "state of its health". Moreover, the information coming from one of the two states is too small to make decisions.
Therefore, I suggest to have a look at transition models. Transition models are constructed with the help of a sequence of bisimilar states. As I am fiddling with transition models and I am not an expert in this field, you may ask yourself where the states of hmm gracel, brain and cancer come from. The transition matrix of the concept of normalization can be divided into two parts: one part corresponding to the normalization of an agent and the other one corresponding to the normalization of all agents. There are two states where the agent normalization depends on the state of the other agents. We can deduct from this, that the observed series are effects of norm- states, i.e. each agent transitions to state 1 or state 2 as consequence of the state of the other agents. d2c66b5586