factor graphs造句

• A constraint graph is a factor graph where all factors are constraints.
• With a corresponding factor graph shown on the right.
• Any Bayesian network or Markov random field can be represented as a factor graph.
• Observe that the factor graph has a tree.
• Below is a graph fragment of an example LDPC code using Forney's factor graph notation.
• Belief propagation on the tanner graph or factor graph to decode Sudoku codes is discussed in by Sayir.
• A "'factor graph "'is a bipartite graph representing the factorization of a function.
• The book provides a framework for developing iterative algorithms for digital receivers, exploiting the power of factor graphs.
• In the case when the factor graph is a tree, the belief propagation algorithm will compute the exact marginals.
• Indeed, Bayesian Programming is more general than Bayesian networks and has a power of expression equivalent to probabilistic factor graphs.
• The max-product algorithm for factor graphs can be viewed as a generalization of the arc-consistency algorithm for constraint processing.
• There are more detailed generalized compressibility factor graphs based on as many as 25 or more different pure gases, such as the Nelson-Obert graphs.
• Since the transmitted message must have satisfied the code constraints, the message can be represented by writing the received message on the top of the factor graph.
• When the factor graph has cycles, such an optimal scheduling does not exist, and a typical choice is to update all messages simultaneously at each iteration.
• The iterative decoding algorithm works best when there are not short cycles in the factor graph that represents the decoder; the interleaver is chosen to avoid short cycles.
• The generalized compressibility factor graphs may be considerably in error for strongly polar gases which are gases for which the centers of positive and negative charge do not coincide.
• One of the important success stories of factor graphs and the sum-product algorithm is the decoding of capacity-approaching error-correcting codes, such as LDPC and turbo codes.
• In the case where the factor graph is acyclic ( i . e . is a tree or a forest ), these estimated marginal actually converge to the true marginals in a finite number of iterations.
• In probability theory and its applications, factor graphs are used to represent factorization of a probability distribution function, enabling efficient computations, such as the computation of marginal distributions through the sum-product algorithm.
• The Hammersley Clifford theorem shows that other probabilistic models such as Bayesian networks and Markov networks can be represented as factor graphs; the latter representation is frequently used when performing inference over such networks using belief propagation.