Institution

# Stanford University

Education•Stanford, California, United States•

About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.

Topics: Population, Transplantation, Cancer, Gene, Health care

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01 Jan 1997TL;DR: SelfSelf-Efficacy (SE) as discussed by the authors is a well-known concept in human behavior, which is defined as "belief in one's capabilities to organize and execute the courses of action required to produce given attainments".

Abstract: Albert Bandura and the Exercise of Self-Efficacy Self-Efficacy: The Exercise of Control Albert Bandura. New York: W. H. Freeman (www.whfreeman.com). 1997, 604 pp., $46.00 (hardcover). Enter the term "self-efficacy" in the on-line PSYCLIT database and you will find over 2500 articles, all of which stem from the seminal contributions of Albert Bandura. It is difficult to do justice to the immense importance of this research for our theories, our practice, and indeed for human welfare. Self-efficacy (SE) has proven to be a fruitful construct in spheres ranging from phobias (Bandura, Jeffery, & Gajdos, 1975) and depression (Holahan & Holahan, 1987) to career choice behavior (Betz & Hackett, 1986) and managerial functioning (Jenkins, 1994). Bandura's Self-Efficacy: The Exercise of Control is the best attempt so far at organizing, summarizing, and distilling meaning from this vast and diverse literature. Self-Efficacy may prove to be Bandura's magnum opus. Dr. Bandura has done an impressive job of summarizing over 1800 studies and papers, integrating these results into a coherent framework, and detailing implications for theory and practice. While incorporating prior works such as Social Learning Theory (Bandura, 1977) and "Self-efficacy mechanism in human agency" (Bandura, 1982), Self-Efficacy extends these works by describing results of diverse new research, clarifying and extending social cognitive theory, and fleshing out implications of the theory for groups, organizations, political bodies, and societies. Along the way, Dr. Bandura masterfully contrasts social cognitive theory with many other theories of human behavior and helps chart a course for future research. Throughout, B andura' s clear, firm, and self-confident writing serves as the perfect vehicle for the theory he espouses. Self-Efficacy begins with the most detailed and clear explication of social cognitive theory that I have yet seen, and proceeds to delineate the nature and sources of SE, the well-known processes via which SE mediates human behavior, and the development of SE over the life span. After laying this theoretical groundwork, subsequent chapters delineate the relevance of SE to human endeavor in a variety of specific content areas including cognitive and intellectual functioning; health; clinical problems including anxiety, phobias, depression, eating disorders, alcohol problems, and drug abuse; athletics and exercise activity; organizations; politics; and societal change. In Bandura's words, "Perceived self-efficacy refers to beliefs in one's capabilities to organize and execute the courses of action required to produce given attainments" (p. 3). People's SE beliefs have a greater effect on their motivation, emotions, and actions than what is objectively true (e.g., actual skill level). Therefore, SE beliefs are immensely important in choice of behaviors (including occupations, social relationships, and a host of day-to-day behaviors), effort expenditure, perseverance in pursuit of goals, resilience to setbacks and problems, stress level and affect, and indeed in our ways of thinking about ourselves and others. Bandura affirms many times that humans are proactive and free as well as determined: They are "at least partial architects of their own destinies" (p. 8). Because SE beliefs powerfully affect human behaviors, they are a key factor in human purposive activity or agency; that is, in human freedom. Because humans shape their environment even as they are shaped by it, SE beliefs are also pivotal in the construction of our social and physical environments. Bandura details over two decades of research confirming that SE is modifiable via mastery experiences, vicarious learning, verbal persuasion, and interpretation of physiological states, and that modified SE strongly and consistently predicts outcomes. SE beliefs, then, are central to human self-determination. STRENGTHS One major strength of Self-Efficacy is Bandura's ability to deftly dance from forest to trees and back again to forest, using specific, human examples and concrete situations to highlight his major theoretical premises, to which he then returns. …

46,839 citations

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01 Jan 1991

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.

Abstract: Preface to the Second Edition. Preface to the First Edition. Acknowledgments for the Second Edition. Acknowledgments for the First Edition. 1. Introduction and Preview. 1.1 Preview of the Book. 2. Entropy, Relative Entropy, and Mutual Information. 2.1 Entropy. 2.2 Joint Entropy and Conditional Entropy. 2.3 Relative Entropy and Mutual Information. 2.4 Relationship Between Entropy and Mutual Information. 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information. 2.6 Jensen's Inequality and Its Consequences. 2.7 Log Sum Inequality and Its Applications. 2.8 Data-Processing Inequality. 2.9 Sufficient Statistics. 2.10 Fano's Inequality. Summary. Problems. Historical Notes. 3. Asymptotic Equipartition Property. 3.1 Asymptotic Equipartition Property Theorem. 3.2 Consequences of the AEP: Data Compression. 3.3 High-Probability Sets and the Typical Set. Summary. Problems. Historical Notes. 4. Entropy Rates of a Stochastic Process. 4.1 Markov Chains. 4.2 Entropy Rate. 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph. 4.4 Second Law of Thermodynamics. 4.5 Functions of Markov Chains. Summary. Problems. Historical Notes. 5. Data Compression. 5.1 Examples of Codes. 5.2 Kraft Inequality. 5.3 Optimal Codes. 5.4 Bounds on the Optimal Code Length. 5.5 Kraft Inequality for Uniquely Decodable Codes. 5.6 Huffman Codes. 5.7 Some Comments on Huffman Codes. 5.8 Optimality of Huffman Codes. 5.9 Shannon-Fano-Elias Coding. 5.10 Competitive Optimality of the Shannon Code. 5.11 Generation of Discrete Distributions from Fair Coins. Summary. Problems. Historical Notes. 6. Gambling and Data Compression. 6.1 The Horse Race. 6.2 Gambling and Side Information. 6.3 Dependent Horse Races and Entropy Rate. 6.4 The Entropy of English. 6.5 Data Compression and Gambling. 6.6 Gambling Estimate of the Entropy of English. Summary. Problems. Historical Notes. 7. Channel Capacity. 7.1 Examples of Channel Capacity. 7.2 Symmetric Channels. 7.3 Properties of Channel Capacity. 7.4 Preview of the Channel Coding Theorem. 7.5 Definitions. 7.6 Jointly Typical Sequences. 7.7 Channel Coding Theorem. 7.8 Zero-Error Codes. 7.9 Fano's Inequality and the Converse to the Coding Theorem. 7.10 Equality in the Converse to the Channel Coding Theorem. 7.11 Hamming Codes. 7.12 Feedback Capacity. 7.13 Source-Channel Separation Theorem. Summary. Problems. Historical Notes. 8. Differential Entropy. 8.1 Definitions. 8.2 AEP for Continuous Random Variables. 8.3 Relation of Differential Entropy to Discrete Entropy. 8.4 Joint and Conditional Differential Entropy. 8.5 Relative Entropy and Mutual Information. 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information. Summary. Problems. Historical Notes. 9. Gaussian Channel. 9.1 Gaussian Channel: Definitions. 9.2 Converse to the Coding Theorem for Gaussian Channels. 9.3 Bandlimited Channels. 9.4 Parallel Gaussian Channels. 9.5 Channels with Colored Gaussian Noise. 9.6 Gaussian Channels with Feedback. Summary. Problems. Historical Notes. 10. Rate Distortion Theory. 10.1 Quantization. 10.2 Definitions. 10.3 Calculation of the Rate Distortion Function. 10.4 Converse to the Rate Distortion Theorem. 10.5 Achievability of the Rate Distortion Function. 10.6 Strongly Typical Sequences and Rate Distortion. 10.7 Characterization of the Rate Distortion Function. 10.8 Computation of Channel Capacity and the Rate Distortion Function. Summary. Problems. Historical Notes. 11. Information Theory and Statistics. 11.1 Method of Types. 11.2 Law of Large Numbers. 11.3 Universal Source Coding. 11.4 Large Deviation Theory. 11.5 Examples of Sanov's Theorem. 11.6 Conditional Limit Theorem. 11.7 Hypothesis Testing. 11.8 Chernoff-Stein Lemma. 11.9 Chernoff Information. 11.10 Fisher Information and the Cram-er-Rao Inequality. Summary. Problems. Historical Notes. 12. Maximum Entropy. 12.1 Maximum Entropy Distributions. 12.2 Examples. 12.3 Anomalous Maximum Entropy Problem. 12.4 Spectrum Estimation. 12.5 Entropy Rates of a Gaussian Process. 12.6 Burg's Maximum Entropy Theorem. Summary. Problems. Historical Notes. 13. Universal Source Coding. 13.1 Universal Codes and Channel Capacity. 13.2 Universal Coding for Binary Sequences. 13.3 Arithmetic Coding. 13.4 Lempel-Ziv Coding. 13.5 Optimality of Lempel-Ziv Algorithms. Compression. Summary. Problems. Historical Notes. 14. Kolmogorov Complexity. 14.1 Models of Computation. 14.2 Kolmogorov Complexity: Definitions and Examples. 14.3 Kolmogorov Complexity and Entropy. 14.4 Kolmogorov Complexity of Integers. 14.5 Algorithmically Random and Incompressible Sequences. 14.6 Universal Probability. 14.7 Kolmogorov complexity. 14.9 Universal Gambling. 14.10 Occam's Razor. 14.11 Kolmogorov Complexity and Universal Probability. 14.12 Kolmogorov Sufficient Statistic. 14.13 Minimum Description Length Principle. Summary. Problems. Historical Notes. 15. Network Information Theory. 15.1 Gaussian Multiple-User Channels. 15.2 Jointly Typical Sequences. 15.3 Multiple-Access Channel. 15.4 Encoding of Correlated Sources. 15.5 Duality Between Slepian-Wolf Encoding and Multiple-Access Channels. 15.6 Broadcast Channel. 15.7 Relay Channel. 15.8 Source Coding with Side Information. 15.9 Rate Distortion with Side Information. 15.10 General Multiterminal Networks. Summary. Problems. Historical Notes. 16. Information Theory and Portfolio Theory. 16.1 The Stock Market: Some Definitions. 16.2 Kuhn-Tucker Characterization of the Log-Optimal Portfolio. 16.3 Asymptotic Optimality of the Log-Optimal Portfolio. 16.4 Side Information and the Growth Rate. 16.5 Investment in Stationary Markets. 16.6 Competitive Optimality of the Log-Optimal Portfolio. 16.7 Universal Portfolios. 16.8 Shannon-McMillan-Breiman Theorem (General AEP). Summary. Problems. Historical Notes. 17. Inequalities in Information Theory. 17.1 Basic Inequalities of Information Theory. 17.2 Differential Entropy. 17.3 Bounds on Entropy and Relative Entropy. 17.4 Inequalities for Types. 17.5 Combinatorial Bounds on Entropy. 17.6 Entropy Rates of Subsets. 17.7 Entropy and Fisher Information. 17.8 Entropy Power Inequality and Brunn-Minkowski Inequality. 17.9 Inequalities for Determinants. 17.10 Inequalities for Ratios of Determinants. Summary. Problems. Historical Notes. Bibliography. List of Symbols. Index.

45,034 citations

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01 Feb 2009TL;DR: In this paper, the authors describe the process of inducting theory using case studies from specifying the research questions to reaching closure, which is a process similar to hypothesis-testing research.

Abstract: Building Theories From Case Study Research - This paper describes the process of inducting theory using case studies from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.

40,005 citations

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TL;DR: An integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment is presented and findings are reported from microanalyses of enactive, vicarious, and emotive mode of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes.

Abstract: The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.

38,007 citations

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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.

Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

35,225 citations

##### Authors

Showing all 127468 results

Name | H-index | Papers | Citations |
---|---|---|---|

Eric S. Lander | 301 | 826 | 525976 |

George M. Whitesides | 240 | 1739 | 269833 |

Yi Cui | 220 | 1015 | 199725 |

Yi Chen | 217 | 4342 | 293080 |

David Miller | 203 | 2573 | 204840 |

David Baltimore | 203 | 876 | 162955 |

Edward Witten | 202 | 602 | 204199 |

Irving L. Weissman | 201 | 1141 | 172504 |

Hongjie Dai | 197 | 570 | 182579 |

Robert M. Califf | 196 | 1561 | 167961 |

Frank E. Speizer | 193 | 636 | 135891 |

Thomas C. Südhof | 191 | 653 | 118007 |

Gad Getz | 189 | 520 | 247560 |

Mark Hallett | 186 | 1170 | 123741 |

John P. A. Ioannidis | 185 | 1311 | 193612 |