In a three-category model, the maximum-likelihood estimate for each probability is its observed count divided by the sample size. These estimates sum to one and respect the probability constraints.
Conditions: The model has three categories.; The estimates are derived using maximum likelihood.; The observed counts and sample size are known.
In a three-category model, the maximum-likelihood estimate for each probability is its observed count divided by the sample size. These estimates sum to one and respect the probability constraints.
Conditions: The model has three categories.; The estimates are derived using maximum likelihood.; The observed counts and sample size are known.
A Markov chain is a mathematical system that undergoes transitions from one state to another according to certain probabilistic rules. Its defining characteristic is the memoryless property: the probability of transitioning to any particular state depends solely on the current state and time elapsed, not on the sequence of events that preceded it.
Conditions: The future state depends only on the present state.; Transition probabilities are defined between states.; The system is a Markov chain.
A Markov chain is a mathematical system that undergoes transitions from one state to another according to certain probabilistic rules. Its defining characteristic is the memoryless property: the probability of transitioning to any particular state depends solely on the current state and time elapsed, not on the sequence of events that preceded it.
Conditions: The future state depends only on the present state.; Transition probabilities are defined between states.; The system is a Markov chain.