It is well known that occurrence counts of words in documents are often modeled poorly by standard distributions like the binomial or Poisson. Observed counts vary more than simple models predict, prompting the use of overdispersed models like Gamma-Poisson or Beta-binomial mixtures as robust alternatives. Another deﬁciency of standard models is due to the fact that most words never occur in a given document, resulting in large amounts of zero counts. We propose using zeroinﬂated models for dealing with this, and evaluate competing models on a Naive Bayes text classiﬁcation task.
[ Team LiB ] 7.8 Generate Blocks Generate statements allow Verilog code to be generated dynamically at elaboration time before the simulation begins. This facilitates the creation of parametrized models
Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Image Resolution Enhancement via Data-Driven Parametric Models in the Wavelet Space
Introduction to Creo Parametric 3.0 includes introduction to Creo Parametric 3.0; introduction to the Creo Parametric Basic Modeling Process; understanding Creo Parametric Concepts; using the Creo Parametric Interface; selecting Geometry, Features, and Models; editing Geometry, Features, and Models; creating Sketcher Geometry; using Sketcher Tools; creating Sketches for Features and some things else.
The goal of this course is to teach you how to use the SolidWorks mechanical design automation software to build parametric models of parts and assemblies and how to make simple drawings of those parts and assemblies.
Linear parametric models of stationary random processes, whether signal or noise, have been found
to be useful in a wide variety of signal processing tasks such as signal detection, estimation, filtering,
We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semantically equivalent syntactic structures, and (3) predict predicate-argument relations between the fragments.
In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known related language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character mappings and highlevel morphemic correspondences.
In this paper* I will argue for a model of grammatical processing that is based on uniform processing and knowledge sources. The main feature of this model is to view parsing and generation as two strongly interleaved tasks performed by a single parametrized deduction process. It will be shown that this view supports flexible and efficient natural language processing.
The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.
The paper describes an experimental model of syntactic structure generation starting from the limited fragment of semantics that deals with the quantitative values of object parameters. To present the input information the basic semantic units of four types are proposed:"object", "parameter", "function" and "constant".
Since the publication of my book Mathematical Statistics (Shao, 2003), I
have been asked many times for a solution manual to the exercises in my
book. Without doubt, exercises form an important part of a textbook
on mathematical statistics, not only in training students for their research
ability in mathematical statistics but also in presenting many additional
results as complementary material to the main text.
Chapter 13b: Answer key about COST MANAGEMENT
1. Answer: c Both the cost and accuracy of parametric models vary widely. They are most likely to be reliable when the historical information used to develop the model was accurate, the parameters used in the model are readily quantiﬁable, and the model is scalable (i.e., it works as well for a very large project as for a very small one). 2. Answer: b An analogous estimate is one that is arrived at by taking a project or part of a project that is already completed and adjusting the cost on the basis...
Knowing that the plant parameters can vary within their lower and upper bounds, this
parametric uncertainty is formulated as an additive perturbation in the transfer function
matrix. It is important to note that the controller be designed with respect to worst case
uncertainty for each λij. This can be achieved by performing an optimization procedure
given by (61) for 200 frequencies. Here an element by element uncertainty bound model is
used for the characterization of upper bound of the uncertainty matrix. Then wij , which
satisfies (62) for each λij is given in matrix form as,...
The Signal Processing Toolbox is a collection of tools built on the MATLAB®
numeric computing environment. The toolbox supports a wide range of signal
processing operations, from waveform generation to filter design and
implementation, parametric modeling, and spectral analysis. The toolbox
provides two categories of tools:
Parametric representation of shapes, mechanical components modeling with 3D visualization techniques using object oriented programming, the well known golden ratio application on vertical and horizontal displacement investigations of the ground surface, spatial modeling and simulating of dynamic continuous fluid flow process, simulation model for waste-water treatment, an interaction of tilt and illumination conditions at flight simulation and errors in taxiing performance, plant layout optimal plot plan, atmospheric modeling for weather prediction, a stochastic search method that explores ...