Fisher and Mahalanobis described Statistics as the key technology of the twentieth century. Since then Statistics has evolved into a ﬁeld that has many applications in all sciences and areas of technology, as well as in most areas of decision making such as in health care, business, federal statistics and legal proceedings. Applications in statistics such as inference for Causal effects, inferences about the spatio- temporal processes, analysis of categorical and survival data sets and countless other functions play an essential role in the present day world.
A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as “if objects come after verbs, then adjectives come after nouns.” Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study.
objective or subjective, when making decisions under uncertainty. This is especially true
when the consequences of the decisions can have a significant impact, financial or
otherwise. Most of us make everyday personal decisions this way, using an intuitive process
based on our experience and subjective judgments.
Mainstream statistical analysis, however, seeks objectivity by generally restricting the
information used in an analysis to that obtained from a current set of clearly relevant data.
Event recognition methods can be roughly categorized into
model-based methods and appearance-based techniques.
Model-based approaches relied on various models, includ-
ing HMM , coupled HMM , and Dynamic Bayesian
Network , to model the temporal evolution. The
relationships among different body parts and regions are
also modeled in , , in which object tracking needs to
be conducted at first before model learning.
This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially inﬁnite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows signiﬁcant improvements of the models when compared against two baselines for this task.