Add to ORKGThis paper explores the role of memory in decision making in dynamic environments. We examine the inference problem faced by an agent with bounded memory who receives a sequence of signals from a hidden Markov model. We show that the optimal symmetric memory rule may be deterministic. This result contrasts sharply with Hellman and Cover (1970) and Wilson (2004) and solves, for the context of a hidden Markov model, an open question posed by Kalai and Solan (2003).Bounded Memory; Hidden Markov Model; Randomization.
Hidden Markov models (HMMs for short) are a type of stochastic models that have been used for a numb...
We consider an infinite collection of agents who make decisions, sequentially, about an unknown unde...
This paper studies the implications of bounded memory in a strate-gic context. In particular, we loo...
This paper explores the role of memory in decision making in dynamic environments. We examine the in...
This paper explores the value of memory in decision making in dynamic environments. We examine the d...
This paper addresses the problem of constructing good action selection policies for agents acting in...
We present a learning algorithm for hidden Markov models with continuous state and observa-tion spac...
We present a learning algorithm for hidden Markov models with continuous state and observation space...
We consider a hidden Markov model with multiple observation processes, one of which is chosen at eac...
Hidden Markov Models (HMM) are interpretable statistical models that specify distributions over sequ...
We study detection of random signals corrupted by noise that over time switch their values (states) ...
This paper studies the problem of ergodicity of transition probabilitymatrices in Marko-vian models,...
We propose and analyze a distribution learning algorithm for variable memory length Markov processes...
The training objectives of the learning object are: 1) To interpret a Hidden Markov Model (HMM); and...
We present a framework for learning in hidden Markov models with distributed state representations...
Hidden Markov models (HMMs for short) are a type of stochastic models that have been used for a numb...
We consider an infinite collection of agents who make decisions, sequentially, about an unknown unde...
This paper studies the implications of bounded memory in a strate-gic context. In particular, we loo...
This paper explores the role of memory in decision making in dynamic environments. We examine the in...
This paper explores the value of memory in decision making in dynamic environments. We examine the d...
This paper addresses the problem of constructing good action selection policies for agents acting in...
We present a learning algorithm for hidden Markov models with continuous state and observa-tion spac...
We present a learning algorithm for hidden Markov models with continuous state and observation space...
We consider a hidden Markov model with multiple observation processes, one of which is chosen at eac...
Hidden Markov Models (HMM) are interpretable statistical models that specify distributions over sequ...
We study detection of random signals corrupted by noise that over time switch their values (states) ...
This paper studies the problem of ergodicity of transition probabilitymatrices in Marko-vian models,...
We propose and analyze a distribution learning algorithm for variable memory length Markov processes...
The training objectives of the learning object are: 1) To interpret a Hidden Markov Model (HMM); and...
We present a framework for learning in hidden Markov models with distributed state representations...
Hidden Markov models (HMMs for short) are a type of stochastic models that have been used for a numb...
We consider an infinite collection of agents who make decisions, sequentially, about an unknown unde...
This paper studies the implications of bounded memory in a strate-gic context. In particular, we loo...