A Markov chain or Markov process 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. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC).
The Edo period or Tokugawa period is the period between 1603 and 1867 in the history of Japan, when Japan was under the rule of the Tokugawa shogunate and the country's 300 regional daimyo. Emerging from the chaos of the Sengoku period, the Edo period was characterized by economic growth, strict social order, isolationist foreign policies, a stable population, perpetual peace, and popular enjoyment of arts and culture, colloquially referred to as Oedo.
The Muromachi period or Muromachi era, also known as the Ashikaga period or Ashikaga era, is a division of Japanese history running from approximately 1336 to 1573. The period marks the governance of the Muromachi or Ashikaga shogunate ( or ), which was officially established in 1338 by the first Muromachi shōgun, Ashikaga Takauji, two years after the brief Kenmu Restoration (1333–1336) of imperial rule was brought to a close. The period ended in 1573 when the 15th and last shogun of this line, Ashikaga Yoshiaki, was driven out of the capital in Kyoto by Oda Nobunaga.
A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.
In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with the model that would otherwise be intractable. For this reason, in the fields of predictive modelling and probabilistic forecasting, it is desirable for a given model to exhibit the Markov property.
