
Agnostic Classification of Markovian Sequences
... sequences and show how to construct a typical random sample of the source. More precisely, let x and y be two sequences, generated by the Markov processes with distributions P and Q, respectively. We present a novel algorithm for the generation of a typical sequence of an approximation to the most l ...
... sequences and show how to construct a typical random sample of the source. More precisely, let x and y be two sequences, generated by the Markov processes with distributions P and Q, respectively. We present a novel algorithm for the generation of a typical sequence of an approximation to the most l ...
notes as
... Trading off the goodness of fit against the complexity of the model • It is intuitively obvious that you can only expect a model to generalize well if it explains the data surprisingly well given the complexity of the model. • If the model has as many degrees of freedom as the data, it can fit the ...
... Trading off the goodness of fit against the complexity of the model • It is intuitively obvious that you can only expect a model to generalize well if it explains the data surprisingly well given the complexity of the model. • If the model has as many degrees of freedom as the data, it can fit the ...
2. Probability
... Choose two people at random. What is the probability that they were both born on December 9? Here the sample space is the set of birthdays: the birthday of the first person chosen, then the birthday of the second person chosen. By the Fundamental principle of Counting, there are 365 × 365 elements i ...
... Choose two people at random. What is the probability that they were both born on December 9? Here the sample space is the set of birthdays: the birthday of the first person chosen, then the birthday of the second person chosen. By the Fundamental principle of Counting, there are 365 × 365 elements i ...