Existing word similarity measures are not robust to data sparseness since they rely only on the point estimation of words’ context proﬁles obtained from a limited amount of data. This paper proposes a Bayesian method for robust distributional word similarities. The method uses a distribution of context proﬁles obtained by Bayesian estimation and takes the expectation of a base similarity measure under that distribution.
Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes...
Over the last decade, a Bayesian network has become a popular representation for
encoding uncertain expert knowledge in expert systems. A Bayesian network is a
graphical model for probabilistic relationships among a set of variables. It is a
graphical model that encodes probabilistic relationships among variables of interest.
When used in conjunction with statistical techniques, the graphical model has several
advantages for data modeling. So what do Bayesian networks and Bayesian methods
have to offer? There are at least four benefits described in the following....
This book addresses state-of-the-art systems and achievements in various topics in the research field of speech and language technologies. Book chapters are organized in different sections covering diverse problems, which have to be solved in speech recognition and language understanding systems. In the first section machine translation systems based on large parallel corpora using rule-based and statistical-based translation methods are presented.
Think Bayes is an introduction to Bayesian statistics using computational methods and Python programming language. Bayesian statistics are usually presented mathematically, but many of the ideas are easier to understand computationally. Contents: Bayes's Theorem; Computational statistics; Tanks and Trains; Urns and Coins; Odds and addends; Hockey; The variability hypothesis; Hypothesis testing.
Learning a tree substitution grammar is very challenging due to derivational ambiguity. Our recent approach used a Bayesian non-parametric model to induce good derivations from treebanked input (Cohn et al., 2009), biasing towards small grammars composed of small generalisable productions. In this paper we present a novel training method for the model using a blocked Metropolis-Hastings sampler in place of the previous method’s local Gibbs sampler.
We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram language models as well as word dictionaries. Bayesian inference is performed on our model using an efﬁcient sampling technique. We evaluate the quality of the Bayesian decipherment output on simple and homophonic letter substitution ciphers and show that unlike a previous approach, our method consistently produces almost 100% accurate decipherments. ...
Sitting at the intersection between statistics and machine learning, Dynamic Bayesian Networks have been applied with much success in many domains, such as speech recognition, vision, and computational biology. While Natural Language Processing increasingly relies on statistical methods, we think they have yet to use Graphical Models to their full potential. In this paper, we report on experiments in learning edit distance costs using Dynamic Bayesian Networks and present results on a pronunciation classiﬁcation task. ...
We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods for n-gram language models.
Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is ﬁnding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...
ADVANCED TEXTS IN ECONOMETRICS General Editors Manuel Arellano Guido Imbens Grayham E. Mizon Adrian Pagan Mark Watson Advisory Editor C. W. J. Granger.Other Advanced Texts in conometrics ARCH: Selected Readings Edited by Robert F. Engle Asymptotic Theory for Integrated Processes By H. Peter Boswijk Bayesian Inference in Dynamic Econometric Models By Luc Bauwens, Michel Lubrano, and Jean-Fran¸ois Richard c Co-tegration, Error Correction, and the Econometric Analysis of Non-Stationary Data By Anindya Banerjee, Juan J. ...
Phương pháp tiếp cận địa lý để phân tích các sự kiện hiếm trong dân số nhỏ và ứng dụng trong các mẫu Homicide Kiểm tra
Khi lãi được sử dụng như là ước tính cho một nguy cơ tiềm ẩn của một sự kiện hiếm (ví dụ, ung thư, AIDS, giết người), những người với một dân số cơ sở nhỏ có cao không đúng và do đó ít đáng tin cậy.
To deal with the lack of reported information, we propose a novel approach to obtain
the exposure contained in the net position in interest-rate derivatives. We specify a state
space model of a bank’s derivatives trading strategy. We then use Bayesian methods to
estimate the bank’s strategy using the joint distribution of interest rates, bank fair and
notional values as well as bid-ask spreads. Intuitively, the identiﬁcation of the bank’s
strategy relies on whether the net position (per dollar notional) gains or loses in value
over time, together with the history of rates.
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 A Two-Stage Bayesian Network Method for 3D Human Pose Estimation from Monocular Image
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.
Medical Statistics at a Glance is directed at undergraduate
medical students, medical researchers, postgraduates in the
biomedical disciplines and at pharmaceutical industry personnel.
All of these individuals will, at some time in their
professional lives, be faced with quantitative results (their
own or those of others) that will need to be critically
evaluated and interpreted, and some, of course, will have to
pass that dreaded statistics exam! A proper understanding
of statistical concepts and methodology is invaluable for
12 Bayesian Procedures
A powerful set of procedures for estimating discrete choice models has been developed within the Bayesian tradition. The breakthough concepts were introduced by Albert and Chib (1993) and McCulloch and Rossi (1994) in the context of probit, and by Allenby and Lenk (1994) and Allenby (1997)
Almost all research in the social and behavioral sciences, and also in eco
nomic and marketing research, criminological research, and social medical
research deals with the analysis of categorical data. Categorical data are
quantified as either nominal or ordinal variables. This volume is a collec
tion of up-to-date studies on modern categorical data analysis methods,
emphasizing their application to relevant and interesting data sets.
I have three vivid memories about learning statistics as an undergraduate
that all involve misconceptions. Firstly, I remember my lecturer telling
me that, after obtaining a result that was not statistically significant,
I should conclude that timber harvesting did not have an effect (on what,
I cannot remember). While the logic was flawed, I have since realized
that it is a misconception shared by many ecologists.