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TABLE I THE BELIEF PROPAGATION-GUIDED DECIMATION ALGORITHM.

in Solving Constraint Satisfaction Problems through Belief Propagation-guided decimation
by Andrea Montanari, Federico Ricci-tersenghi, Guilhem Semerjian

Table 1. Stratified sampling message updating and belief computing algorithm

in Sequential stratified sampling belief propagation for multiple targets tracking
by Jianru Xue, Nanning Zheng, Xiaopin Zhong
"... In PAGE 5: ... We adopt a stratified sampler similar to PAMPAS but in its sequential version. The stratified sampling propagation that consists of message updating and belief computation, detail is described in Table1 . Each message in BP is represented by a set of weighted particles, i.... In PAGE 8: ...337 Then following equations from (17-19), message propagation and belief computa- tion is described in Table1 . the sequential Monte Carlo belief propagation is shown in Table 2.... ..."
Cited by 1

Table 2. Sequential stratified sampling for belief propagation

in Sequential stratified sampling belief propagation for multiple targets tracking
by Jianru Xue, Nanning Zheng, Xiaopin Zhong
Cited by 1

Table 2. Raising related beliefs

in A Logic-Based Approach for Adaptive Information Filtering Agents
by Raymond Lau, Arthur H.M. ter Hofstede, Peter D. Bruza 2001
"... In PAGE 7: ...e. exp(B)) can be tabulated in Table2 . Based on the maxi-adjustment algorithm, B+( ; i)( ) = i if B( ) i lt; degree(B; ! ).... ..."
Cited by 2

Table I. Summary of results from #28Weiss and Freeman, 1999#29 regarding belief propagation after convergence.

in Learning Low-Level Vision
by William T. Freeman, Egon C. Pasztor, Owen T. Carmichael 2000
Cited by 238

Table 1* Back Propagation Genetic Algorithm

in STATISTICAL APPLICATIONS OF NEURAL NETWORKS
by Sangit Chatterjee, Matthew Laudato 1995
"... In PAGE 11: ... In each case network architecture is that of Figure 1. The parameters used in the training of the network are given in Table1 . Two measures of fit, namely the sum of squares deviation (Ssq.... In PAGE 17: ... Table1 . The parameter settings to run the neural network using both the back propagation and the genetic algorithms to run all three problems are given.... ..."

Table 1: Algorithm for planning in low-dimensional belief space.

in Abstract
by Nicholas Roy, Geoffrey Gordon
"... In PAGE 4: ... Our conversion algorithm is a variant of the Augmented MDP, or Coastal Navigation algorithm [9], using belief features instead of entropy. Table1 outlines the steps of this... ..."

Table 4: Algorithm for belief expansion with random action selection

in Anytime point-based approximations for large pomdps
by Joelle Pineau, Geoffrey Gordon, Sebastian Thrun 2006
"... In PAGE 16: ... In the case of Stochastic Simulation with Random Action (SSRA), the action selected for forward simulation is picked (uniformly) at random from the full action set. Table4 summarizes the belief expansion procedure for SSRA. First, a state s is drawn from the belief distribution b.... ..."
Cited by 2

Table 4: Algorithm for belief expansion with random action selection

in Anytime point-based approximations for large pomdps
by Joelle Pineau, Geoffrey Gordon, Sebastian Thrun 2006
"... In PAGE 16: ... In the case of Stochastic Simulation with Random Action (SSRA), the action selected for forward simulation is picked (uniformly) at random from the full action set. Table4 summarizes the belief expansion procedure for SSRA. First, a state s is drawn from the belief distribution b.... ..."
Cited by 2

Table 1: Belief state structure for PSDG inference.

in Probabilistic State-Dependent Grammars for Plan Recognition
by David V. Pynadath, Michael P. Wellman 2000
"... In PAGE 6: ... Our specialized inference algorithms instead maintain a much smaller belief state that summa- rizes this probability distribution by exploiting the indepen- dence properties of the PSDG model and the restricted set of PSDG queries. Table1 lists the probability tables that form Bt, the belief state for time t, where Et represents all evidence (Qt 2 Rt) received through time t. The belief component, Bt T (`; q), represents a boolean random vari- able that is true if and only if the expansion of the symbol at level ` terminates at time t.... ..."
Cited by 45
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