A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

A new method for visualizing the behavior of an iterative decoder is presented. This method is applied to regular and irregular low-density parity-check codes. Several types of decoding errors are investigated and suggestions are made for improving the iterative decoder to avoid such errors. Furthermore, structural properties of parity-check matrices that influence the decoding performance are identified.

Iterative decoding of two-dimensional systematic convolutional codes has been termed “turbo” (de)coding. Using log-likelihood algebra, we show that any decoder can he used which accepts soft inputs-including a priori values-and delivers soft outputs that can he split into three terms: the soft

Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order

It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual “expert ” models makes it hard to generate samples from the combined model but easy to infer the values

A new method for visualizing the behavior of an iterative decoder is presented. This method is applied to regular and irregular low-density parity-check codes. Several types of decoding errors are investigated and suggestions are made for improving the iterative decoder to avoid such errors. Furthermore, structural properties of parity-check matrices that influence the decoding performance are identified.

: logit, generalized extreme value (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, the method of simulated

of the space–time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space–time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple

nothing directly to do with coding or decoding will show that in some sense belief propagation "converges with high probability to a near-optimum value" of the desired belief on a class of loopy DAGs Progress in the analysis of loopy belief propagation has been made for the case of networks

present in real-world programs, and describe how to effectively capture and exploit it in order to perform load value prediction. Temporal and spatial locality are attributes of storage locations, and describe the future likelihood of references to those locations or their close neighbors. In a similar