Books: 'A priori data'

1

Stroobandt, Dirk. A priori wire length estimates for digital design. Boston: Kluwer Academic Publishers, 2001.

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2

Kovtoni͡uk, N. F. Fotochuvstvitelʹnye MDP-pribory dli͡a preobrazovanii͡a izobrazheniĭ. Moskva: "Radio i svi͡azʹ", 1990.

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3

Henryk, Maciejewski. Predictive modelling in high-dimensional data: Prior domain knowledge-based approaches. Wrocław: Oficyna Wydawnicza Politechniki Wrocławskiej, 2013.

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4

D, Orli͡a︡nskiĭ A., and Gosudarstvennyĭ komitet SSSR po gidrometeorologii i kontroli͡u︡ prirodnoĭ sredy., eds. Pribory, ustanovki, avtomatizat͡s︡ii͡a︡ v ėksperimentalʹnoĭ meteorologii. Moskva: Moskovskoe otd-nie Gidrometeoizdata, 1985.

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5

S, Kaniovskiĭ S., Bodner Vasiliĭ Afanasʹevich, Seleznev A. V, and Moskovskiĭ institut priborostroenii͡a︡, eds. Tochnye pribory i izmeritelʹnye sistemy. Moskva: MIP, 1992.

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6

S, Kaniovskiĭ S., and Moskovskiĭ institut priborostroenii͡a︡, eds. Tochnye pribory i izmeritelʹnye sistemy. Moskva: MIP, 1990.

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7

Gorn, L. S. Programmno-upravli͡a︡emye pribory i kompleksy dli͡a︡ izmerenii͡a︡ ionizirui͡u︡shchego izluchenii͡a︡. Moskva: Ėnergoatomizdat, 1985.

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8

Gorn, L. S. Programmno-upravli︠a︡emye pribory i kompleksy dli︠a︡ izmerenii︠a︡ ionizirui︠u︡shchego izluchenii︠a︡. Moskva: Ėnergoatomizdat, 1985.

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9

Scott Jones, Julie. Learn to Clean and Prepare Scale Data Prior to Descriptive Analysis Using Data From the General Social Survey (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605105.

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10

Scott Jones, Julie. Learn to Clean and Prepare Categorical Data Prior to Descriptive Analysis Using Data From the General Social Survey (2018). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605037.

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11

National Center for Education Statistics, ed. Prior-to-secondary school course classification system: School codes for the exchange of data (SCED). Washington, DC: U.S. Dept. of Education, National Center for Education Statistics, 2011.

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12

Indonesia. Badan Penyehatan Perbankan Nasional. IBRA program for meeting objectives prior to IBRA sunset date, Feb. 2004. Jakarta: Indonesian Bank Restructuring Agency, 2001.

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13

Scott Jones, Julie. Learn to Clean and Prepare Categorical Data Prior to a Regression Analysis in SPSS Using Data From the Health Survey for England (2011) (Teaching Dataset). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605334.

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14

Artemov, N. I., and V. A. Kisling. Problemy i perspektivy avtomatizat͡sii proizvodstva i upravlenii͡a na pedprii͡atii͡akh priboro- i mashinostroenii͡a: Tezisy dokladov Vsesoi͡uznoĭ nauchno-tekhnicheskoĭ konferent͡sii : sekt͡sii͡a 1, sistemnye aspekty avtomatizat͡sii proizvodstva i upravlenii͡a. Permʹ: [s.n.], 1987.

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15

Byers, Alden S. The antecedents of Jacob Byers, b. prior 1754-7, d. 1801, and Ann (Nancy) Douglass, b. prior 1764, d. 1833: Containing various genealogical data on associated Scottish, Scotch-Irish, and English Byers families of colonial Pennsylvania : a search for identity. Palm Springs, CA: A.S. Byers, 1991.

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16

Pond, Nathan Gillett. Inscriptions on tombstones in Milford, Conn., erected prior to 1800, together with a few of aged persons who died after that date. New Haven: Printed for the Society, 1986.

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17

Pacific Rim International Conference on Artificial Intelligence (11th 2010 Daegu, Korea). PRICAI 2010: Trends in artificial intelligence : 11th Pacific Rim International Conference on Artificial Intelligence, Daegu, Korea, August 30 - September 2, 2010 : proceedings. Berlin: Springer, 2010.

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18

United, States Congress House Committee on Energy and Commerce Subcommittee on Commerce Trade and Consumer Protection. H.R. 5777, the "Best Practices Act," and H.R. _______, a discussion draft to require notice to and consent of an individual prior to the collection and disclosure of certain personal information relating to that individual: Hearing before the Subcommittee on Commerce, Trade, and Consumer Protection of the Committee on Energy and Commerce, House of Representatives, One Hundred Eleventh Congress, second session, July 22, 2010. Washington: U.S. Government Printing Office, 2013.

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19

Zabelina, Ol'ga, Irina Omel'chenko, Anna Mayorova, and Ekaterina Safonova. Human resource Development in the Digital Age: Strategic Challenges, Challenges, and Opportunities. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1243772.

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The monograph, based on the identification of trends and problems of changes in the demand and supply of skills, as well as the study of modern mechanisms of their formation and actualization, substantiates the priority areas of human resources development in the Russian Federation that meet the strategic challenges of the period of digital transformation of the labor sphere. The authors identify and systematize current and future trends related to changes in the demand for professions and skills in the Russian and global labor markets. The directions of transformation of the demand for skills and professions in the conditions of digitalization of the economy, skills and professions of the future are determined. Quantitative and qualitative imbalances and trends in labor supply and demand in the Russian labor market are identified (based on statistical analysis of data from 2009-2019). The features and problems of supply and demand of professions/skills in the segments of the Russian labor market covered by Internet recruitment are identified (based on data from resume parsing and vacancies of Internet recruitment portals in 2018 and 2020). Methodological approaches to identifying widely-and poorly-demanded skills are proposed and tested during the competence analysis of labor supply and demand using Big Data technologies.the competence profile of the vacancies of the professional core and extra - skills. An innovative author's approach to assessing the potential of skills capitalization — a possible increase in the salary of an applicant due to the expansion of the set of skills that he has-is proposed and tested. The current policy directions of formation and improvement of skills of the population in the Russian Federation are identified and systematized. The strategic challenges of the period of digital transformation of the labor sphere facing the Russian Federation and the priority areas of human resources development that meet these challenges are identified. The conclusions and recommendations can be used in the work of the Ministry of Labor of Russia, Rostrud, the Ministry of Education and Science of Russia, the Ministry of Education of Russia, government authorities, employment services of the Russian regions, as well as organizations of the professional education system.

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20

Chance, Kelly, and Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.

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This chapter explores several of the most common and useful approaches to atmospheric data fitting as well as the process of using air mass factors to produce vertical atmospheric column abundances from line-of-sight slant columns determined by data fitting. An atmospheric spectrum or other type of atmospheric sounding is usually fitted to a parameterized physical model by minimizing a cost function, usually chi-squared. Linear fitting, when the model of the measurements is linear in the model parameters is described, followed by the more common nonlinear fitting case. For nonlinear fitting, the standard Levenberg-Marquardt method is described, followed by the use of optimal estimation, one of several retrieval methods that make use of a priori information to providing regularization for the solution. In the context of optimal estimation, weighting functions, contribution functions, and averaging kernels are described. The Twomey-Tikhonov regularization procedure is presented. Correlated parameters, with the important example of Earth’s atmospheric ozone, are discussed.

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21

Stroobandt, Dirk. Priori Wire Length Estimates for Digital Design. Springer London, Limited, 2011.

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22

Stroobandt, Dirk. A priori Wire Length Estimates for Digital Design. Springer, 2001.

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23

Stroobandt, Dirk. A Priori Wire Length Estimates for Digital Design. Springer, 2012.

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24

Stroobandt, Dirk. A Priori Wire Length Estimates for Digital Design. Springer, 2012.

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25

Tecuci, Gheorghe, David A. Schum, Dorin Marcu, and Mihai Boicu. Knowledge Engineering: Building Cognitive Assistants for Evidence-Based Reasoning. Cambridge University Press, 2016.

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26

Tecuci, Gheorghe, David A. Schum, Dorin Marcu, and Mihai Boicu. Knowledge Engineering: Building Cognitive Assistants for Evidence-Based Reasoning. Cambridge University Press, 2016.

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27

Knowledge Engineering: Building Personal Learning Assistants for Evidence-Based Reasoning. Cambridge University Press, 2016.

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28

Moshonov, Hada. Bayesian model chekcing - prior-data conflict. 2007, 2007.

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29

Peters, Pam. The lexicography of English usage. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198808206.003.0003.

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The lexicography of English usage is often discussed as being prescriptive or descriptive, but only rarely is it analysed in terms of how usage writers use language evidence in exploring issues of current and changing usage, and whether their methodology is empirical or otherwise. This chapter discusses two twenty-first-century approaches to the use of evidence in usage writing: the selective, a priori use of citations by Bryan Garner to support his ‘Language Change Index’ in Modern American Usage (3rd edn, 2009); and the wealth of data contained in the GloWbE corpus (2012) and others created by Mark Davies, available to quantify usage trends worldwide. Corpus evidence on the assimilation of Latin borrowings, e.g. use of data in singular agreement, shows this is relatively less advanced in the US than elsewhere, which aligns with its stigmatization in American academic discourse.

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30

Wilson, Mark. Believers in The Land of Glory. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198803478.003.0006.

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We employ words like “cause” both to structure an investigative architecture and to register concrete physical data in light of that strategic background. As a result, “cause”’s referential significance becomes very complicated as the word progressively enters fresh patches of application. Jim Woodward’s studies have demonstrated the central role that considerations of manipulative control play in mapping out the contours of these strategic specializations. In contrast, analytic metaphysicians have attempted to carve out an a priori pre-science of causal necessity that falters through ignoring these developmental considerations. All in all, this essay presents a critique of necessitarian thinking not unlike that offered by Quine in his well-known “Two Dogmas of Empiricism.”

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31

Tochnye pribory i izmeritelʹnye sistemy. Moskva: MIP, 1991.

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32

Griffiths, Thomas L. Formalizing Prior Knowledge in Causal Induction. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.38.

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Prior knowledge plays a central role in causal induction, helping to explain how people are capable of identifying causal relationships from small amounts of data. Bayesian inference provides a way to characterize the influence that prior knowledge should have on causal induction, as well as an explanation for how that knowledge could itself be acquired. Using the theory-based causal induction framework of Griffiths and Tenenbaum (2009), this chapter reviews recent work exploring the relationship between prior knowledge and causal induction, highlighting some of the ways in which people’s expectations about causal relationships differ from approaches to causal learning in statistics and computer science.

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33

Hugh, Beale, Bridge Michael, Gullifer Louise, and Lomnicka Eva. Part IV Priorities, 14 Exceptions to the Nemo Dat Rule. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198795568.003.0014.

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This chapter illustrates how a person who acquires a legal interest in good faith and without notice takes priority over the holder of an equitable interest. The person acquiring the legal interest must provide value and not have actual or constructive notice at the time that the interest was acquired. If, however, the interest, when acquired, was initially equitable, the holder of the interest can at a later date acquire the legal interest and gain priority over the holder of a prior equitable interest, despite the fact that at the time the legal interest was acquired, the holder knew of the prior equitable interest. This is the doctrine of tabula in naufragio.

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34

Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customized Bayesian computational methods. In the experiment, a subject was asked to perform simple arithmetic operations for a period of three hours. Prior to the experiment, the subject was confirmed to be alert. After the experiment ended, the subject was fatigued. The study demonstrates that Bayesian analysis is useful for real time detection of cognitive fatigue.

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35

Hungary in the decade prior to EU accession: Analysing the data, 1990-2002. 2nd ed. Budapest: AKII, 2004.

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36

Taberlet, Pierre, Aurélie Bonin, Lucie Zinger, and Eric Coissac. DNA metabarcoding data analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198767220.003.0008.

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DNA metabarcoding generates huge amounts of data containing noise introduced by molecular methods. Chapter 8 “DNA metabarcoding data analysis” discusses the analytic steps and available software to curate and evaluate DNA metabarcoding data prior to final ecological analyses. It provides command lines to perform primary analyses of Illumina sequencing data with the OBITools, ranging from read assignment to samples to the formation of molecular operational taxonomic units (MOTUs) and their assignment to a taxon through comparison against reference databases. Chapter 8 also develops several methods to further curate sequencing data from contaminants or dysfunctional PCRs by using DNA extraction, PCR, and sequencing blank controls as well as PCR/biological replicates. It also presents several classical analyses to ensure that the diversity of the sample or the study site is appropriately covered. Finally, this chapter considers what conclusions on biodiversity and ecological processes can be really drawn from metabarcoding data.

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37

El-Nasr, Magy Seif, Alessandro Canossa, Truong-Huy D. Nguyen, and Anders Drachen. Game Data Science. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192897879.001.0001.

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This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book introduces different methods borrowing from different fields including human computer interaction, machine learning, and data science, focusing on methods and techniques used by both industry and researchers within the field of games. The book examples and case studies specifically focus on gameplay log data. The book takes a practical stance on the subject by discussing theoretical foundation, practical approaches, and delves deeply into the different techniques proposed and used through labs, examples, and comprehensive surveys of various case studies from both industry and academia. Topics range from simple approaches to more advanced ones. No prior knowledge is required. The book is developed to be self contained and can be used as a good way to introduce the reader to data science and how it is applied to the filed of games.

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38

Safar, Jiri G. Prion Paradigm of Human Neurodegenerative Diseases Caused by Protein Misfolding. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190233563.003.0005.

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Data accumulated from different laboratories argue that a growing number of proteins causing neurodegeneration share certain characteristics with prions. Prion-like particles were produced from synthetic amyloid beta (Aβ‎) peptides of Alzheimer’s disease (AD), from recombinant α‎-synuclein linked to Parkinson’s disease (PD), and from recombinant tau associated with frontotemporal dementias (FTD). Evidence from human prions reveals that variable disease phenotypes, rates of propagation, and targeting of different brain structures are determined by distinct conformers (strains) of pathogenic prion protein. Recent progress in the development of advanced biophysical tools identified the structural characteristics of Aβ‎ in the brain cortex of phenotypically diverse AD patients and thus allowed an investigation of the prion paradigm of AD. The findings of distinctly structured strains of human brain Aβ‎, forming a unique spectrum of oligomeric particles in the cortex of rapidly progressive cases, implicates these structures in variable rates of propagation in the brain.

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39

AlJaroudi, Wael. Risk Assessment Before Noncardiac Surgery. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0014.

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Perioperative risk assessment is essential in screening patients before noncardiac surgery. Cardiovascular complications such as fatal and non-fatal myocardial infarction (MI), ventricular arrhythmia, pulmonary edema, and stroke are important in-hospital causes of morbidity and mortality intra and post-operatively. The optimal approach is to identify patients at increased risk so that appropriate testing and therapeutic interventions are undertaken a priori to minimize such risk. The initial preoperative evaluation includes identification of surgery-specific risk, patient exercise functional capacity and clinical risk profile. Patients with major predictors of events such as acute coronary syndromes, recent MI, unstable arrhythmia, and severe valvular disease warrant further management and optimization that often lead to delaying surgery. Those with three or more predictors (history of ischemic heart disease, compensated heart failure, diabetes, renal insufficiency, or history of cerebrovascular disease) undergoing high- risk surgery often require stress testing. Although data from randomized prospective trials are lacking, numerous studies have demonstrated the utility of myocardial perfusion imaging (MPI) for determination of perioperative cardiac risk. The goal of this chapter is to review the use of MPI for preoperative risk assessment and the recommendations from the current guidelines. The focus will be on short-term and long-term prognosis including special groups such as after coronary stenting and before vascular surgery, liver and renal transplantation.

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40

History of the Priory of Coldingham from the Earliest Date to the Present Time. Creative Media Partners, LLC, 2023.

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41

History of the Priory of Coldingham from the Earliest Date to the Present Time. Creative Media Partners, LLC, 2023.

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42

Bailey, Jane, and Sara Shayan. Systematic Government Access to Private-Sector Data in Canada. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190685515.003.0007.

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This chapter focuses on Canadian law as it applies to government access to private-sector data. The Canadian Charter of Rights and Freedoms implicitly provides constitutional protection of privacy by prohibiting unreasonable search and seizure by the state (s. 8) and by limiting government intrusion on life, liberty and security of the person (s. 7). With some exceptions, the Charter requires law enforcement agencies to seek prior authorization before accessing personal information. However, Canada’s national security intelligence agencies are subject to more relaxed standards. The Privacy Act regulates federal government institutions’ relationship with personal information, whereas the private sector is regulated by the Personal Information and Protection of Electronic Documents Act. However, numerous exceptions in both statutes allow for (and in some cases encourage), information sharing between private-sector and state entities.

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43

Maxwell, Winston J. Systematic Government Access to Private-Sector Data in France. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190685515.003.0002.

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This chapter focuses on France’s legal framework for access to private-sector data by law enforcement and intelligence agencies. Post-9/11, France enacted provisions to require telecommunications operators and providers of hosting services to retain significant amounts of metadata. The French laws on data retention went beyond the scope of the now-invalidated EU directive on data retention, and the French laws remain on the books today in spite of a recent CJEU decision holding that similar laws in the UK and Sweden violate fundamental rights. France’s intelligence agencies have wide-ranging powers to collect data and conduct interceptions without prior court approval, including the right to analyze metadata of all French Internet users to detect suspicious patterns of behavior. In 2016, the French Constitutional Court invalidated a 25-year-old law permitting intelligence authorities to conduct untargeted monitoring of radio transmissions without supervision; so far, other provisions of government surveillance laws have survived constitutional challenge.

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44

Ponte, María de, and Kepa Korta. New thoughts about old facts. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198714217.003.0010.

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In a famous paper by Prior, ontological, epistemic, and semantic considerations are entangled in a way that creates the illusion of an ontological argument about the nature of time. This chapter defends the thesis that Prior’s argument is best interpreted as a “knowledge argument,” similar to that raised by Frank Jackson against physicalism. At a linguistic level, the authors argue that an utterance like “Thank goodness that is over [now]” expresses the same proposition as “Thank goodness the date of the conclusion of the root canal is Friday, June 15, 1954,” when uttered on the same date. At the epistemic level, it is argued that the two are associated with different motivating thoughts. At the ontological level, the authors reject the assumption that the proposition related to the utterance “Thank goodness that is over [now]” and its associated thought require the existence of A-properties.

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45

Peres , Tanya M., and Aaron Deter-Wolf, eds. The Cumberland River Archaic of Middle Tennessee. University Press of Florida, 2019. http://dx.doi.org/10.5744/florida/9781683400837.001.0001.

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Prior to 2010, the only major literature on the manifestation of the Shell Mound Archaic in the Middle Cumberland River Valley of Tennessee was an unpublished dissertation and technical or avocational reports. Recent research by the coeditors reveals that there are nearly forty Archaic shell-bearing sites in the region. This volume brings together multiple lines of evidence to more fully examine a major cultural phase that has been virtually overlooked in the professional literature. We approach this topic by incorporating data and discussions of recent research at Archaic shell-bearing sites in the western Middle Cumberland River Valley combined with contemporary examinations of prior investigations, which until now have been difficult for scholars to access. The data presented in this volume are a testament to the sustainability of riverine adaptions in antiquity.

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46

Essick, John. The Python-Based Laboratory. Oxford University PressOxford, 2024. https://doi.org/10.1093/9780191998478.001.0001.

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Abstract The Python-Based Laboratory: A Hands-On Guide for Scientists and Engineers provides a learn-by-doing approach to acquiring the Python programming skills needed to implement computer-controlled experimental work. The Python-Based Laboratory leads its readers to mastery of the popular, open-source Python computer language in its role as a powerful laboratory tool by carrying out interesting and relevant projects that explore the acquisition, production, analysis, and presentation of digitized waveforms. Readers, who are assumed to have no prior computer programming or Python background, begin writing meaningful programs in the first few pages. The Python-Based Laboratory can be used as a textbook for science and engineering instructional laboratory students who are being taught up-to-date Python-based experimental skills. The book also works well as a self-study guide for professional laboratory researchers, industrial engineers, hobbyists, and electronics enthusiasts seeking to automate tasks using Python. Topics covered include the control of data-acquisition devices (including multifunction data-acquisition hardware and IEEE 488.2-interfaced stand-alone instruments), data file storage and presentation, digitized data concepts (such as resolution, sampling frequency, and aliasing), data analysis techniques (curve fitting and fast Fourier transform), and building a graphical user interface (GUI) using the Tkinter toolkit that is included as part of the Python Standard Library. As readers work their way through the book, they build several computer-based instruments, including a DC voltmeter, digital oscilloscope, DC voltage source, waveform generator, blinking LED array, digital thermometer, spectrum analyzer, and frequency meter. Each chapter concludes with a Do Yourself project and a Use It! example as well as a healthy selection of homework-style problems, allowing readers to test their understanding and further develop their Python-based experimentation skills.

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47

Pricai 2012 Trends In Artificial Intelligence 12th Pacific Rim International Conference On Artificial Intelligence Kuching Malaysia September 3 7 2012 Proceedings. Springer, 2012.

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48

The history of ancient Windsor, Connecticut: Including East Windsor, South Windsor, and Ellington, prior to 1768, the date of their separation from the ... prior to 1800 (A Heritage classic). Heritage Books, 1997.

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49

Walsh, Bruce, and Michael Lynch. Measuring Multivariate Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0030.

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This chapter extends many of the results from Chapter 29 on single trait-fitness associations to the multiple trait setting. It examines the estimate of multivariate fitness surfaces, starting with quadratic surfaces and then moving to nonparametric versions (which assume no a prior functional form). It also examines path analysis, the analysis of missing data, and multilevel selection.

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50

Kruschke, John K., and Wolf Vanpaemel. Bayesian Estimation in Hierarchical Models. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.13.

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Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.

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Books on the topic 'A priori data'

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