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TRANSITION MATRIX THEORY
"... Abstract. In this article we present a unification of the theory of algebraic, singular, topological and directional transition matrices by introducing the (generalized) transition matrix which encompasses each of the previous four. Some transition matrix existence results are presented as well as v ..."
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Abstract. In this article we present a unification of the theory of algebraic, singular, topological and directional transition matrices by introducing the (generalized) transition matrix which encompasses each of the previous four. Some transition matrix existence results are presented as well
Transition Matrix Monte Carlo
, 2008
"... Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations. In this paper, we describe an complementary a ..."
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Cited by 1 (0 self)
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approach to extracting information from Monte Carlo simulations that uses the matrix of transition probabilities. Combining the Transition Matrix with an Nfold way simulation technique produces an extremely flexible and efficient approach to rather general Monte Carlo simulations. 1 1
GENERALIZED TOPOLOGICAL TRANSITION MATRIX.
"... Abstract. This article represents a major step in the unification of the theory of algebraic, topological and singular transition matrices by introducing a definition which is a generalization that encompasses all of the previous three. When this more general transition matrix satisfies the addition ..."
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Abstract. This article represents a major step in the unification of the theory of algebraic, topological and singular transition matrices by introducing a definition which is a generalization that encompasses all of the previous three. When this more general transition matrix satisfies
The Infinite Hidden Markov Model
 Machine Learning
, 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
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Cited by 637 (41 self)
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. These three hyperparameters define a hierarchical Dirichlet process capable of capturing a rich set of transition dynamics. The three hyperparameters control the time scale of the dynamics, the sparsity of the underlying statetransition matrix, and the expected number of distinct hidden states in a finite
Directional Transition Matrix
"... We present a generalization of topological transition matrices introduced in [6]. 1. ..."
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We present a generalization of topological transition matrices introduced in [6]. 1.
STEPS IN CREATING TRANSITION MATRIX
"... Organizations track systems over time. Usually static reports are generated monthly, yearly or quarterly, and the reports are compared across time. On the reports are categories of information, usually counts, and other information. Objects in the population may change status over time. They might b ..."
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Organizations track systems over time. Usually static reports are generated monthly, yearly or quarterly, and the reports are compared across time. On the reports are categories of information, usually counts, and other information. Objects in the population may change status over time. They might be added to the inventory, discharged from treatment, or moved within the system. Children graduate from grade school to middle school, stock is moved from warehouse to store shelf, patients improve from hospital to outpatient care. When analyzing a population of such objects it is useful to not just consider the categorical counts, but the change of the population
Transition Matrix Monte Carlo Method
, 2001
"... We present a formalism of the transition matrix Monte Carlo method. A stochastic matrix in the space of energy can be estimated from Monte Carlo simulation. This matrix is used to compute the density of states, as well as to construct multicanonical and equalhit algorithms. We discuss the performa ..."
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Cited by 11 (0 self)
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We present a formalism of the transition matrix Monte Carlo method. A stochastic matrix in the space of energy can be estimated from Monte Carlo simulation. This matrix is used to compute the density of states, as well as to construct multicanonical and equalhit algorithms. We discuss
On the Transition Matrix of the Flow Mechanism in a MultiEchelon
, 2012
"... Abstract ⎯ This paper is concerned with deriving, using logistic and Markov chain theoretic methodologies, a transition matrix for a multiechelon educational system. The explanatory variables of the logistic model are the school differential variables, and the transition matrix of the Markov chain ..."
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Abstract ⎯ This paper is concerned with deriving, using logistic and Markov chain theoretic methodologies, a transition matrix for a multiechelon educational system. The explanatory variables of the logistic model are the school differential variables, and the transition matrix of the Markov chain
Randomized Gossip Algorithms
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... Motivated by applications to sensor, peertopeer, and ad hoc networks, we study distributed algorithms, also known as gossip algorithms, for exchanging information and for computing in an arbitrarily connected network of nodes. The topology of such networks changes continuously as new nodes join a ..."
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Cited by 532 (5 self)
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stochastic matrix characterizing the algorithm. Designing the fastest gossip algorithm corresponds to minimizing this eigenvalue, which is a semidefinite program (SDP). In general, SDPs cannot be solved in a distributed fashion; however, exploiting problem structure, we propose a distributed subgradient
Results 1  10
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