Physical theories allow us to make predictions: given a complete description of a physical
system, we can predict the outcome of some measurements. This problem of predicting
the result of measurements is called the modelization problem, the simulation problem,
or the forward problem. The inverse problem consists of using the actual result of some
measurements to infer the values of the parameters that characterize the system.
While the forward problemhas (in deterministic physics) a unique solution, the inverse
problem does not.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Estimation of variance components of threshold characters by marginal posterior modes and means via
CHAPTER 37 OLS With Random Constraint. A Bayesian considers the posterior density the full representation of the information provided by sample and prior information. Frequentists have discoveered that one can interpret the parameters of this density as estimators of the key unknown parameters
This book was written for graduate students and researchers in statistics and the
social sciences. Our intent in writing the book was to bridge the gap between
recent theoretical developments in statistics and the application of these methods
to ordinal data. Ordinal data are the most common form of data acquired in the
social sciences, but the analysis of such data is generally performed without regard
to their ordinal nature.
Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: variance components in a mixed linear model using Gibbs sampling
Marginal inferences about
University of Wisconsin-Madison, Department of Meat and Animal Science, Madison, WI 53706-1284, USA
Summary - Arguing from a Bayesian viewpoint, Gianola and Foulley (1990) derived a new method for estimation of variance components in a mixed linear model: varia...
Automatic error detection is desired in the post-processing to improve machine translation quality. The previous work is largely based on conﬁdence estimation using system-based features, such as word posterior probabilities calculated from N best lists or word lattices. We propose to incorporate two groups of linguistic features, which convey information from outside machine translation systems, into error detection: lexical and syntactic features.
Word and n-gram posterior probabilities estimated on N-best hypotheses have been used to improve the performance of statistical machine translation (SMT) in a rescoring framework. In this paper, we extend the idea to estimate the posterior probabilities on N-best hypotheses for translation phrase-pairs, target language n-grams, and source word reorderings. The SMT system is self-enhanced with the posterior knowledge learned from Nbest hypotheses in a re-decoding framework.