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Introduction to Probability and Statistics from a Bayesian Viewpoint
Abstract
The two parts of this book treat probability and statistics as mathematical disciplines and with the same degree of rigour as is adopted for other branches of applied mathematics at the level of a British honours degree. They contain the minimum information about these subjects that any honours graduate in mathematics ought to know. They are written primarily for general mathematicians, rather than for statistical specialists or for natural scientists who need to use statistics in their work. No previous knowledge of probability or statistics is assumed, though familiarity with calculus and linear algebra is required. The first volume takes the theory of probability sufficiently far to be able to discuss the simpler random processes, for example, queueing theory and random walks. The second volume deals with statistics, the theory of making valid inferences from experimental data, and includes an account of the methods of least squares and maximum likelihood; it uses the results of the first volume.
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Cited by
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., et al. (2018) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1343-3 · Cited by 1,627
Confidence in judgment: persistence of the illusion of validity
Einhorn, H. J., & Hogarth, R. M. (1978) · Psychological Review · doi:10.1037/0033-295x.85.5.395 · Cited by 1,209
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
Wiecki, T. V., Sofer, I., & Frank, M. J. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00014 · Cited by 1,047
A Predictive Approach to Model Selection
Geisser, S., & Eddy, W. F. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481632 · Cited by 988
A Statistical Model for Positron Emission Tomography
Vardi, Y., Shepp, L. A., & Kaufman, L. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10477119 · Cited by 863
Metadata
- Authors
- Lindley, D. V.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 1965-01-01
- OpenAlex
- W2101170002
- Cited by
- 760
- References
- 0
- FWCI
- 18.58
- Type
- book
- Language
- en
- Open access
- closed
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- Lindley, D. V. (1965). Introduction to Probability and Statistics from a Bayesian Viewpoint. Cambridge University Press eBooks. https://doi.org/10.1017/cbo9780511662973
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.