CBOW learns to predict the word by the context window(+nt -nt words) around it by taking the max probability of the word that fits this context(the most frequent word it learns).Therefore words with infrequent values will not work great with this technique. Skip-Gram is inverted to CBOW, thus learns toContinue Reading
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobservable (i.e. hidden) states. In simpler Markov models (like a Markov chain), the state is directly visible to the observer, and therefore the state transition probabilities areContinue Reading
A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. (“memorylessness”)Continue Reading
* Is a discrete time stochastic control process for decision making in situations where outcomes are partly random and partly under the control of a decision maker. * At each discrete time step, the process is in some state s, and the decision maker may choose any action a thatContinue Reading
A prior probability distribution of an uncertain quantity is the probability distribution that would express one’s beliefs about this quantity before some evidence is taken into account. A prior can be determined from past information, such as previous experiments.Continue Reading
Expresses how likely particular values of statistics or a random variables are for a given set of observations. Indexes the family of joint probability distribution of the random sample evaluated at the given observations.Continue Reading
Bayes’ Theorem – Is the probability Event A will happen given (|) Event B happened. P(A|B) = P(B|A) * P(A) / P(B) For example (Assume we have 3 out of 4 probabilities): Calculate a probability that an event A(Eating) will happen given (|) that an event B(Exercising) happened. P(A|B) = P(B|A)Continue Reading