We introduce a class of reinforcement models where, at each time step t, one ¿rst chooses a random subset A_t of colours (independent of the past) from n colours of balls, and then chooses a colour i from this subset with probability proportional to the number of balls of colour i in the urn raised to the power a > 1. We consider stability of equilibria for such models and establish the existence of phase transitions in a number of examples, including when the colours are the edges of a graph, a context which is a toy model for the formation and reinforcement of neural connections. Keywords: reinforcement model, Pólya urn, stochastic approximation algorithm, stable equilibri
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
We introduce a class of reinforcement models where, at each time step t, one ¿rst chooses a random s...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
\u3cp\u3eWe introduce a class of reinforcement models where, at each time step t, one first chooses ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one ¿rst chooses a random s...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
We introduce a class of reinforcement models where, at each time step t, one ¿rst chooses a random s...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
\u3cp\u3eWe introduce a class of reinforcement models where, at each time step t, one first chooses ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one ¿rst chooses a random s...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
We introduce a class of reinforcement models where, at each time step t, one first chooses a random ...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...
Consider a generalized time-dependent Pólya urn process defined as follows. Let d∈N be the number of...