intTypePromotion=1
ADSENSE

Bayesian statistics

Xem 1-20 trên 26 kết quả Bayesian statistics
  • 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.

    pdf176p ringphone 06-05-2013 64 3   Download

  • (bq) part 2 book "business statistics" has contents: simple linear regression and correlation, multiple regression, time series, forecasting, and index numbers, quality control and improvement, bayesian statistics and decision analysis, sampling methods, multivariate analysis,...and other contents.

    pdf475p bautroibinhyen27 11-05-2017 21 3   Download

  • 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.

    pdf1044p vigro23 28-08-2012 76 19   Download

  • Applied statistics for civil and environmental engineers has many contents: Preliminary Data Analysis, Basic Probability Concepts, Random Variables and Their Properties, Model Estimation and Testing, Methods of Regression and Multivariate Analysis, Frequency Analysis of Extreme Events, Simulation Techniques for Design, Risk and Reliability Analysis, Bayesian Decision Methods and Parameter Uncertainty.

    pdf737p doremon3244 05-06-2014 58 17   Download

  • (BQ) Part 2 book "Mathematical statistics with applications" has contents: Linear regression models, design of experiments, analysis of variance, bayesian estimation and inference, nonparametric tests, empirical methods, some issues in statistical applications - an overview.

    pdf414p bautroibinhyen20 06-03-2017 24 5   Download

  • 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.

    pdf268p banhkem0908 24-11-2012 38 3   Download

  • 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 these needs.

    pdf139p hyperion75 22-01-2013 43 3   Download

  • 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. ...

    pdf8p bunbo_1 17-04-2013 55 3   Download

  • (BQ) Part 1 book "Mathematics and statistics for financial risk management" has contents: Some basic math, probabilities, basic statistics, distributions, multivariate distributions and copulas, bayesian analysis, hypothesis testing and confidence intervals, matrix algebra.

    pdf184p bautroibinhyen20 06-03-2017 36 4   Download

  • 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.

    pdf10p hongdo_1 12-04-2013 29 2   Download

  • Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. ...

    pdf10p hongdo_1 12-04-2013 52 3   Download

  • This paper presents a Bayesian decision framework that performs automatic story segmentation based on statistical modeling of one or more lexical chain features. Automatic story segmentation aims to locate the instances in time where a story ends and another begins. A lexical chain is formed by linking coherent lexical items chronologically. A story boundary is often associated with a significant number of lexical chains ending before it, starting after it, as well as a low count of chains continuing through it.

    pdf4p hongphan_1 15-04-2013 28 1   Download

  • We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. ...

    pdf8p bunbo_1 17-04-2013 36 1   Download

  • 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.

    pdf240p lulanphuong 17-03-2012 48 8   Download

  • 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....

    pdf124p bi_bi1 08-07-2012 51 7   Download

  • Null hypothesis significance testing (NHST) is one of the main research tools in social and behavioral research. It requires the specification of a null hypothesis, an alternative hypothesis, and data in order to test the null hypothesis. The main result of a NHST is a p-value [3]. An example of a null hypothesis and a corresponding alternative hypothesis for a one-way analysis of variance is:

    pdf360p banhkem0908 24-11-2012 46 7   Download

  • 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

    pdf13p leslienguyen 28-10-2010 54 5   Download

  • CHAPTER 17 Causality and Inference. This chapter establishes the connection between critical realism and Holland and Rubin’s modelling of causality in statistics as explained in [Hol86] and [WM83, pp. 3–25] (and the related paper [LN81] which comes from a Bayesian point of view).

    pdf39p leslienguyen 28-10-2010 45 3   Download

  • 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.

    pdf354p cucdai_1 19-10-2012 37 3   Download

  • 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.

    pdf312p phoebe75 01-02-2013 29 2   Download

CHỦ ĐỀ BẠN MUỐN TÌM

ADSENSE

p_strKeyword=Bayesian statistics
p_strCode=bayesianstatistics

nocache searchPhinxDoc

 

Đồng bộ tài khoản
2=>2