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Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos
Artificial intelligence in PET/CT oncologic imaging / / edited by John A. Andreou, Paris A. Kosmidis, and Athanasios D. Gouliamos
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (156 pages)
Disciplina 610.285
Soggetto topico Tomography, Emission
Artificial intelligence - Medical applications
Processament de dades
Tomografia
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 3-031-10090-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- 1: Introduction: Artificial Intelligence (AI) Systems for Oncology -- 1.1 Introduction -- 1.2 Applications -- 1.3 Challenges -- References -- 2: Positron Emission Tomography in Bone and Soft Tissue Tumors -- 2.1 Introduction -- 2.2 Positron Emission Tomography in Sarcomas -- 2.3 Positron Emission Tomography in Gastrointestinal Stromal Tumors -- 2.4 Artificial Intelligence -- 2.5 Conclusion -- References -- 3: PET/CT in Brain Tumors: Current Artificial Intelligence Applications -- 3.1 Introduction -- 3.2 Radiopharmaceuticals -- 3.3 Radiomics in the Study of Brain Malignancies -- 3.4 Identification of Brain Tumors, Molecular Markers, Grading and Prognosis -- 3.4.1 FDG PET -- 3.4.2 MET PET -- 3.4.3 FDOPA PET -- 3.4.4 FET PET -- 3.4.5 FLT PET and Other Tracers -- 3.5 Biopsy Guiding -- 3.6 Radiation Therapy Planning -- 3.7 Treatment Monitoring -- 3.8 Role of PET/CT in Brain Metastases -- References -- 4: Artificial Intelligence in Head and Neck Cancer Patients -- 4.1 Introduction -- 4.2 Artificial Intelligence: Performing Tasks Requiring Human Intelligence -- 4.3 Artificial Intelligence in Medicine -- 4.4 Artificial Intelligence in Oncology: Head and Neck Cancer -- 4.5 Conclusions -- References -- 5: PET-CT in Lung Cancer -- References -- 6: Breast Cancer: PET/CT Imaging -- References -- 7: PET/CT in Gynecologic Cancer -- 7.1 PET/CT with [18F]FDG in Cervical Cancer -- 7.1.1 Initial Diagnosis and Prognosis -- 7.1.2 Initial Staging -- 7.1.3 Radiotherapy Planning -- 7.1.4 Restaging after Treatment -- 7.1.5 Tumor Recurrence -- 7.1.6 Conclusion -- 7.2 PET/CT with [18F]FDG in Endometrial Cancer -- 7.2.1 Initial Diagnosis and Prognosis -- 7.2.2 Initial Staging -- 7.2.3 Tumor Recurrence -- 7.2.4 Conclusion -- 7.3 PET/CT with [18F]FDG in Ovarian Cancer.
7.3.1 Initial Diagnosis: Differentiation Between Malignant and Benign Ovarian Tumors and Prognosis -- 7.3.2 Initial Staging -- 7.3.3 Radiotherapy Planning -- 7.3.4 Restaging After Treatment -- 7.3.5 Tumor Recurrence -- 7.3.6 Conclusion -- References -- 8: PET-CT Staging of Rectal Carcinoma -- 8.1 Introduction -- 8.2 Diagnosis and Initial Staging -- 8.3 Detection and Staging of Recurrent Disease -- 8.4 Monitoring Treatment Response and Planning of Radiation Therapy -- 8.5 PET/CT Radiomics in Rectal Cancer -- 8.6 Conclusions -- References -- 9: Advances in Neuroendocrine Tumor Imaging, Including PET and Artificial Intelligence (AI) -- 9.1 Introduction -- 9.2 SSTR-Based Imaging -- 9.3 Ga-68 SSTR-vs. F18-FDG -- 9.4 Theragnostics in Neuroendocrine Tumors -- 9.5 Tentative Approach to AI in PET/CT Regarding Neuroendocrine Tumors -- References -- 10: PET/CT in the Evaluation of Adrenal Gland Mass -- 10.1 Introduction -- 10.2 PET/CT in Evaluation of Adrenal Masses in Cancer and Noncancer Patients -- 10.3 PET/CT in Primary Tumors' Evaluation -- 10.4 Towards Artificial Intelligence -- 10.5 Conclusion -- References -- 11: PET/CT in Renal Cancer -- 11.1 Introduction -- 11.2 18F-FDG-PET for Renal Cancer Investigation -- 11.2.1 Renal Mass Characterization and Initial Staging -- 11.2.2 Relapse and Evaluation of Treatment Response -- 11.3 Non-FDG Radiopharmaceutical for RCC Imaging -- 11.4 Towards Artificial Intelligence -- References -- 12: PET/CT Findings in Testicular Cancer -- 12.1 Initial Staging: Early Detection of Micrometastases -- 12.2 Response to Treatment Assessment: Residual Mass Characterization -- 12.3 Seminomatous GCTs -- 12.4 Nonseminomatous Germ Cell Tumors -- References -- 13: PET/CT in Prostate Cancer -- 13.1 Introduction -- 13.2 Imaging of Prostate Cancer with PET/CT.
13.3 Artificial Intelligence in the Service of Prostate Cancer Patients -- References -- 14: The Role of 18FDG-PET/CT in Malignant Lymphomas Clinical Implications -- 14.1 Introduction -- 14.2 PET/CT in Initial Staging -- 14.2.1 Role of PET in the Initial Staging of Lymphomas -- 14.2.2 PET in the Assessment of Bone Marrow Involvement -- 14.2.2.1 Hodgkin Lymphoma -- 14.2.2.2 Diffuse Large B Cell and Primary Mediastinal Large B Cell Lymphoma [24-33] -- 14.2.2.3 Other Lymphoma Subtypes -- 14.2.3 Potential Prognostic Impact of Baseline PET Parameters -- 14.3 PET/CT in Response Assessment After Completion of Therapy -- 14.3.1 Criteria for Response Assessment and Definitions of PET Positivity -- 14.3.2 Who Should Have an EOT-PET-Based Response Assessment and When? -- 14.3.3 Clinical Data in Individual Lymphoma Subtypes -- 14.3.3.1 Hodgkin Lymphoma -- 14.3.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.3.3.3 Diffuse Large B Cell Lymphoma -- 14.3.3.4 Follicular Lymphoma -- 14.3.3.5 Mantle Cell Lymphoma -- 14.3.3.6 T Cell Lymphomas -- 14.4 Interim Response Assessment -- 14.4.1 Who Might Benefit from Interim PET-Based Early Response Assessment? -- 14.4.2 Clinical Data in Individual Lymphoma Subtypes -- 14.4.2.1 Hodgkin Lymphoma -- 14.4.3 Is It Reasonable to Modify Treatment of HL in Response to Interim PET Results? -- 14.4.3.1 Diffuse Large B Cell Lymphoma -- 14.4.3.2 Primary Mediastinal Large B Cell Lymphoma -- 14.4.3.3 T Cell Lymphomas -- 14.5 Impact of Interim and EOT-PET on Clinical Practice: Randomized Trials -- 14.5.1 Hodgkin Lymphoma -- 14.5.1.1 Radiotherapy Questions -- 14.5.2 Chemotherapy Questions -- 14.5.3 Aggressive B Cell Lymphomas -- 14.5.3.1 Radiotherapy Questions -- 14.5.3.2 Chemotherapy Questions -- 14.6 PET in the Setting of Autologous Stem Cell Transplantation (ASCT).
14.7 PET in the Era of Novel Agents -- 14.7.1 Programmed Death-1 (PD-1) Inhibitors -- 14.7.2 Chimeric Antigen Receptor (CAR) T cells -- 14.8 Artificial Intelligence in F-FDG-PET/CT Scan -- 14.9 The Role of PET/CT in the Follow-Up of Lymphomas -- 14.10 Conclusions -- References.
Record Nr. UNINA-9910620200803321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (378 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Big data
Mathematical statistics - Data processing
Intel·ligència artificial
Dades massives
Estadística matemàtica
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-031-07155-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910631085803321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
Artificial intelligence, big data and data science in statistics : challenges and solutions in environmetrics, the natural sciences and technology / / Ansgar Steland, Kwok-Leung Tsui, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (378 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Big data
Mathematical statistics - Data processing
Intel·ligència artificial
Dades massives
Estadística matemàtica
Processament de dades
Soggetto genere / forma Llibres electrònics
ISBN 3-031-07155-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996499870303316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Intelligence, Learning and Computation in Economics and Finance / / Ragupathy Venkatachalam, editor
Artificial Intelligence, Learning and Computation in Economics and Finance / / Ragupathy Venkatachalam, editor
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (331 pages)
Disciplina 332.028563
Collana Understanding Complex Systems Series
Soggetto topico Artificial intelligence - Financial applications
Economics - Data processing
Economia
Processament de dades
Intel·ligència artificial
Soggetto genere / forma Llibres electrònics
ISBN 9783031152948
9783031152931
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Perspectives from the Development of Agent-based Modelling in Economics and Finance -- Towards a General Model of Financial Markets -- The U-Mart Futures Exchange Experiment and Her Institutional Design Historically Inherited -- A Bottom-Up Framework for Data-Driven Agent-Based Simulations -- Can News Networks and Topics Influence Assets Return and Volatility? -- Causal Inference and Agent-Based Models -- Finding the Human in Their Stories: Some Thoughts on Digital Humanities Tools -- Interdependence Overcomes the Limitations of Rational Theories of Collective Behavior: The Productivity of Patents by Nations -- Sand Castles and Financial Systems.-Estimation of Agent-Based Models via Approximate Bayesian Computation -- Unravelling Aspects of Decision Making Under Uncertainty -- Logic and Epistemology in Behavioral Economics -- Aggregate Investor Attention and Bitcoin Return: The Machine Learning Approach -- Information and Market Power: An Experimental Investigation into the Hayek Hypothesis -- Algorithmically Learning, Creatively and Intelligently to Play Games -- A Simonian Formalistic Perspective on Collaborative, Distributed Invention -- Modified Sraffan Schemes and Algorithmic Rational Agents.
Record Nr. UNINA-9910672436903321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial neural networks and structural equation modeling : marketing and consumer research applications / / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan, editors
Artificial neural networks and structural equation modeling : marketing and consumer research applications / / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan, editors
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (336 pages)
Disciplina 658.8342
Soggetto topico Consumers - Research - Data processing
Marketing research - Data processing
Neural networks (Computer science)
Consumidors
Màrqueting
Processament de dades
Xarxes neuronals (Informàtica)
Models d'equacions estructurals
Soggetto genere / forma Llibres electrònics
ISBN 981-19-6509-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- List of Figures -- List of Tables -- Artificial Neural Network and Structural Equation Modeling Techniques: Social Commerce -- Artificial Neural Network and Structural Equation Modeling Techniques -- 1 Introduction -- 2 Methodology -- 2.1 Information Sources -- 2.2 Study Selection -- 2.3 Search Strategy -- 2.4 Eligibility Criteria -- 3 Results and Discussion -- 3.1 Crisis -- 3.2 Organizational Theory -- 3.3 Prediction -- 3.4 Perception -- 3.5 Trust -- 3.6 UTAUT Model -- 3.7 Logistics -- 3.8 Mobile Payment -- 3.9 Social Commerce -- 3.10 Wearable Technology -- 3.11 Technology Acceptance -- 3.12 Cloud Computing -- 3.13 Sustainability -- 4 Conclusion -- References -- Determinants of Social Commerce -- 1 Introduction -- 2 Determinant of Social Commerce -- 2.1 Innovation Barriers -- 2.2 Social Support Theory -- 2.3 Social Network Theory -- 2.4 Elaboration Likelihood Model -- 2.5 Commitment Trust Theory -- 2.6 Theory of Social Presence -- 3 Customers' Intentions of Use Social Commerce -- 4 Discussion and Conclusion -- References -- Technology Acceptance Model in Social Commerce -- 1 Introduction -- 2 Discussion -- 3 Dimensions -- 4 Results -- References -- Social Commerce of Rural Communities -- 1 Introduction -- 2 Social Commerce Definition and Background -- 2.1 Social Commerce Definition -- 2.2 Social Commerce Development -- 3 Rural/Rustic Community Concept -- 4 Social Commerce in Rural/Rustic Communities, Perceived Risk and Trust -- 5 Perceived Risk -- 6 Perceived Trust -- 6.1 Education -- 6.2 Information Credibility -- 6.3 Website Quality -- 6.4 Information Quality -- 6.5 Social Support -- 6.6 Innovativeness -- 6.7 Altruism -- 6.8 Sense of Belonging -- 6.9 Electronic Word-of-Mouth (eWOM) -- 6.10 Self-Enhancement -- 7 Conclusion -- References -- Electronic Word of Mouth and Social Commerce -- 1 Introduction.
2 Concept of Electronic Word of Mouth -- 3 Concept of Social Commerce -- 4 The Difference between Electronic Commerce and Social Commerce -- 5 The Relationship Between eWOM and S-Commerce -- 6 Discussion -- 7 Theoretical Implication -- 8 Managerial Implication -- 9 Conclusion -- References -- Determinants of Customer Intentions to Use Social Commerce -- 1 Introduction -- 2 Social Commerce -- 2.1 Concept of Social Commerce -- 2.2 Factors that Influence on Social Commerce -- 2.3 The Customer Purchasing-Decision Process -- 2.4 Customer Intentions and Social Commerce -- 2.5 Enhancing Consumer Online Purchase Intention Through Perspective of Cognitive Evaluation Theory -- 3 Electronic Word Of Mouth (eWOM) -- 3.1 Concept of eWOM -- 3.2 WOM vs. eWOM -- 3.3 The Stages of eWOM -- 3.4 Social Media and Electronic Word of Mouth (eWOM) -- 3.5 Types of Electronic Word of Mouth and Their Impact on Consumer Attitudes -- 4 Conclusion -- References -- Barriers to Using Social Commerce -- 1 Introduction -- 2 Intentions of Customers -- 3 Social Commerce -- 4 Barriers to Using Social Commerce -- 5 Discussion -- 6 Conclusion -- References -- The Role of BlockChain Adoption and Supply Chain Practices on Social Commerce -- 1 Introduction -- 2 What Blockchain Is? -- 3 The Main Components of Blockchain -- 4 Applications of Blockchain -- 5 Blockchain Modules -- 6 The Concept of Supply Chain -- 7 Software and Supply Chain -- 8 Blockchain Technology as a New Driver in Supply Chain -- 9 The Main Technologies Can Support Both Blockchain and Supply Chain -- 10 The Main Challenges and Solutions Related to Blockchain and Supply Chain -- 11 Conclusion -- References -- Mobile Commerce and Social Commerce with the Development of Web 2.0 Technology -- 1 Introduction -- 2 Social Commerce -- 3 Mobile Commerce -- 4 Mobile Commerce in the Hospitality Sector.
5 Mobile Commerce in the Banking Sector -- 6 Mobile Commerce in the Healthcare Sector -- 7 Mobile Commerce in the Economy Sector -- 8 SEM and ANN Approach in Mobile Commerce -- 9 Discussion and Conclusion -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Technology of Marketing -- How Electronic Word of Mouth (eWOM) and Trust Affect Customers' Intention -- 1 Introduction -- 2 Literature Review of Electronic Word of Mouth -- 3 Perceived Crowding -- 4 Wearable Technology and eWOM -- 5 Customers' Intention of Using New Channels -- 6 Discussion -- 7 Theoretical Contributions -- 8 Managerial Contributions -- 9 Conclusion -- References -- Mobile Payment Technology -- 1 Introduction -- 2 Architecture for M-Payments -- 3 Technologies Used for Mobile Payments -- 4 Proximity Payments -- 5 Mobile Payment Protocols -- 6 Mobile System -- 7 Mobile Payment Security Issues -- 8 Detection of Malware -- 9 Conclusion -- References -- The Role of Online Advertising in the Intentions of Customers -- 1 Introduction -- 2 Online Advertising -- 3 Effectiveness and Issues of Online Advertising -- 4 Social Commerce -- 5 Trends in Social Commerce Research -- 6 Online Advertising and Customer Purchase Intentions -- 7 Discussion -- 8 Conclusion -- References -- Intention to Use Social Media Technology Among Customers -- 1 Introduction -- 2 Social Media Technologies, Challenges and Opportunities -- 2.1 Social Media Advantages/Opportunities -- 2.2 Social Media Challenges, Risks and Disadvantages -- 3 Managing Customer Relations on Online Products and Services -- 4 Customer Intentions to Use Social Media Technology -- 5 Discussion and Conclusion -- References -- Barriers to Using Mobile Payment Technology -- 1 Introduction -- 2 Concept of Mobile Payment Technology -- 3 Barriers to Using Mobile Payment Technology -- 3.1 Functional Barriers.
3.2 Usage Barrier -- 3.3 Value Barrier -- 3.4 Risk Barrier -- 3.5 Privacy Risk -- 3.6 Security Risk -- 3.7 Financial Risk -- 3.8 Operational Risk -- 3.9 Psychological Barriers -- 3.10 Tradition Barrier -- 3.11 Image Barrier -- 4 Mobile Payment Technology Acceptance -- 4.1 Prominent Technology-Related Models -- 4.2 User Perception and Experience with Mobile Payments -- 4.3 The Technology Acceptance Model -- 4.4 The Innovation Diffusion Theory -- 4.5 The Decision Process of Innovation Adoption -- 4.6 Resistance to Innovation Model -- 5 Mobile Payment Determinants -- 6 Discussion -- 7 Conclusion -- References -- Green Practices in Marketing -- 1 Introduction -- 2 Literature Development -- 2.1 Basic Things About Green Marketing -- 2.2 Characteristics and Green Marketing Mix -- 2.3 The Role of Green Communication -- 2.4 The Green Push-Pull Communication Strategies -- 2.5 The Green Communication Strategies and Pull-Push Effects -- 2.6 Openness of Green Marketing -- 2.7 Industrial Framework -- 2.8 The Relationship Between Altruism and Customer's Intention to Purchase Green Product -- 2.9 Theory of Planned Behavior -- 2.10 Value-Attitude-Behavior Cognitive Hierarchy in Green Marketing -- 2.11 Open Issues and Challenges of Green Practices in Social Commerce -- 2.12 The Practical Challenge for Greening Marketing Mix -- 2.13 Promotion of Green Marketing -- 3 Conclusion -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Sustainability of Marketing -- Social Responsibility in Marketing -- 1 Introduction -- 2 Marketing Definition -- 3 Corporate Social Responsibility (CSR), Definition and Background -- 4 Types of Corporate Social Responsibility -- 5 Corporate Social Responsibility in Marketing -- 6 CSR Implements in Marketing -- 7 Conclusion -- References -- Sustainability and Social Responsibility in Marketing -- 1 Introduction.
2 Literature Review -- 3 Corporate Social Responsibility in Marketing -- 4 Corporation Efforts in Communicating Their Corporate Social Responsibility Initiatives -- 5 Social Responsibility and Profitability -- 6 Conclusions and Recommendations -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Future Research Directions -- Artificial Neural Network and Structural Equation Modeling in the Future -- 1 Introduction -- 2 Exploring the Relations Between ANN and SEM -- 3 The Benefits of Structural Equation Modeling and Artificial Neural Network Approach -- 4 Artificial Neural Network and Structural Equation Modeling: Systematic Literature Review -- 5 Recommendations of the Previous Studies for SEM and ANN Analysis -- 6 Suggestions for Future Research -- 7 Conclusion -- References.
Record Nr. UNINA-9910631087503321
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial neural networks and structural equation modeling : marketing and consumer research applications / / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan, editors
Artificial neural networks and structural equation modeling : marketing and consumer research applications / / Alhamzah Alnoor, Khaw Khai Wah, Azizul Hassan, editors
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (336 pages)
Disciplina 658.8342
Soggetto topico Consumers - Research - Data processing
Marketing research - Data processing
Neural networks (Computer science)
Consumidors
Màrqueting
Processament de dades
Xarxes neuronals (Informàtica)
Models d'equacions estructurals
Soggetto genere / forma Llibres electrònics
ISBN 981-19-6509-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Contents -- List of Figures -- List of Tables -- Artificial Neural Network and Structural Equation Modeling Techniques: Social Commerce -- Artificial Neural Network and Structural Equation Modeling Techniques -- 1 Introduction -- 2 Methodology -- 2.1 Information Sources -- 2.2 Study Selection -- 2.3 Search Strategy -- 2.4 Eligibility Criteria -- 3 Results and Discussion -- 3.1 Crisis -- 3.2 Organizational Theory -- 3.3 Prediction -- 3.4 Perception -- 3.5 Trust -- 3.6 UTAUT Model -- 3.7 Logistics -- 3.8 Mobile Payment -- 3.9 Social Commerce -- 3.10 Wearable Technology -- 3.11 Technology Acceptance -- 3.12 Cloud Computing -- 3.13 Sustainability -- 4 Conclusion -- References -- Determinants of Social Commerce -- 1 Introduction -- 2 Determinant of Social Commerce -- 2.1 Innovation Barriers -- 2.2 Social Support Theory -- 2.3 Social Network Theory -- 2.4 Elaboration Likelihood Model -- 2.5 Commitment Trust Theory -- 2.6 Theory of Social Presence -- 3 Customers' Intentions of Use Social Commerce -- 4 Discussion and Conclusion -- References -- Technology Acceptance Model in Social Commerce -- 1 Introduction -- 2 Discussion -- 3 Dimensions -- 4 Results -- References -- Social Commerce of Rural Communities -- 1 Introduction -- 2 Social Commerce Definition and Background -- 2.1 Social Commerce Definition -- 2.2 Social Commerce Development -- 3 Rural/Rustic Community Concept -- 4 Social Commerce in Rural/Rustic Communities, Perceived Risk and Trust -- 5 Perceived Risk -- 6 Perceived Trust -- 6.1 Education -- 6.2 Information Credibility -- 6.3 Website Quality -- 6.4 Information Quality -- 6.5 Social Support -- 6.6 Innovativeness -- 6.7 Altruism -- 6.8 Sense of Belonging -- 6.9 Electronic Word-of-Mouth (eWOM) -- 6.10 Self-Enhancement -- 7 Conclusion -- References -- Electronic Word of Mouth and Social Commerce -- 1 Introduction.
2 Concept of Electronic Word of Mouth -- 3 Concept of Social Commerce -- 4 The Difference between Electronic Commerce and Social Commerce -- 5 The Relationship Between eWOM and S-Commerce -- 6 Discussion -- 7 Theoretical Implication -- 8 Managerial Implication -- 9 Conclusion -- References -- Determinants of Customer Intentions to Use Social Commerce -- 1 Introduction -- 2 Social Commerce -- 2.1 Concept of Social Commerce -- 2.2 Factors that Influence on Social Commerce -- 2.3 The Customer Purchasing-Decision Process -- 2.4 Customer Intentions and Social Commerce -- 2.5 Enhancing Consumer Online Purchase Intention Through Perspective of Cognitive Evaluation Theory -- 3 Electronic Word Of Mouth (eWOM) -- 3.1 Concept of eWOM -- 3.2 WOM vs. eWOM -- 3.3 The Stages of eWOM -- 3.4 Social Media and Electronic Word of Mouth (eWOM) -- 3.5 Types of Electronic Word of Mouth and Their Impact on Consumer Attitudes -- 4 Conclusion -- References -- Barriers to Using Social Commerce -- 1 Introduction -- 2 Intentions of Customers -- 3 Social Commerce -- 4 Barriers to Using Social Commerce -- 5 Discussion -- 6 Conclusion -- References -- The Role of BlockChain Adoption and Supply Chain Practices on Social Commerce -- 1 Introduction -- 2 What Blockchain Is? -- 3 The Main Components of Blockchain -- 4 Applications of Blockchain -- 5 Blockchain Modules -- 6 The Concept of Supply Chain -- 7 Software and Supply Chain -- 8 Blockchain Technology as a New Driver in Supply Chain -- 9 The Main Technologies Can Support Both Blockchain and Supply Chain -- 10 The Main Challenges and Solutions Related to Blockchain and Supply Chain -- 11 Conclusion -- References -- Mobile Commerce and Social Commerce with the Development of Web 2.0 Technology -- 1 Introduction -- 2 Social Commerce -- 3 Mobile Commerce -- 4 Mobile Commerce in the Hospitality Sector.
5 Mobile Commerce in the Banking Sector -- 6 Mobile Commerce in the Healthcare Sector -- 7 Mobile Commerce in the Economy Sector -- 8 SEM and ANN Approach in Mobile Commerce -- 9 Discussion and Conclusion -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Technology of Marketing -- How Electronic Word of Mouth (eWOM) and Trust Affect Customers' Intention -- 1 Introduction -- 2 Literature Review of Electronic Word of Mouth -- 3 Perceived Crowding -- 4 Wearable Technology and eWOM -- 5 Customers' Intention of Using New Channels -- 6 Discussion -- 7 Theoretical Contributions -- 8 Managerial Contributions -- 9 Conclusion -- References -- Mobile Payment Technology -- 1 Introduction -- 2 Architecture for M-Payments -- 3 Technologies Used for Mobile Payments -- 4 Proximity Payments -- 5 Mobile Payment Protocols -- 6 Mobile System -- 7 Mobile Payment Security Issues -- 8 Detection of Malware -- 9 Conclusion -- References -- The Role of Online Advertising in the Intentions of Customers -- 1 Introduction -- 2 Online Advertising -- 3 Effectiveness and Issues of Online Advertising -- 4 Social Commerce -- 5 Trends in Social Commerce Research -- 6 Online Advertising and Customer Purchase Intentions -- 7 Discussion -- 8 Conclusion -- References -- Intention to Use Social Media Technology Among Customers -- 1 Introduction -- 2 Social Media Technologies, Challenges and Opportunities -- 2.1 Social Media Advantages/Opportunities -- 2.2 Social Media Challenges, Risks and Disadvantages -- 3 Managing Customer Relations on Online Products and Services -- 4 Customer Intentions to Use Social Media Technology -- 5 Discussion and Conclusion -- References -- Barriers to Using Mobile Payment Technology -- 1 Introduction -- 2 Concept of Mobile Payment Technology -- 3 Barriers to Using Mobile Payment Technology -- 3.1 Functional Barriers.
3.2 Usage Barrier -- 3.3 Value Barrier -- 3.4 Risk Barrier -- 3.5 Privacy Risk -- 3.6 Security Risk -- 3.7 Financial Risk -- 3.8 Operational Risk -- 3.9 Psychological Barriers -- 3.10 Tradition Barrier -- 3.11 Image Barrier -- 4 Mobile Payment Technology Acceptance -- 4.1 Prominent Technology-Related Models -- 4.2 User Perception and Experience with Mobile Payments -- 4.3 The Technology Acceptance Model -- 4.4 The Innovation Diffusion Theory -- 4.5 The Decision Process of Innovation Adoption -- 4.6 Resistance to Innovation Model -- 5 Mobile Payment Determinants -- 6 Discussion -- 7 Conclusion -- References -- Green Practices in Marketing -- 1 Introduction -- 2 Literature Development -- 2.1 Basic Things About Green Marketing -- 2.2 Characteristics and Green Marketing Mix -- 2.3 The Role of Green Communication -- 2.4 The Green Push-Pull Communication Strategies -- 2.5 The Green Communication Strategies and Pull-Push Effects -- 2.6 Openness of Green Marketing -- 2.7 Industrial Framework -- 2.8 The Relationship Between Altruism and Customer's Intention to Purchase Green Product -- 2.9 Theory of Planned Behavior -- 2.10 Value-Attitude-Behavior Cognitive Hierarchy in Green Marketing -- 2.11 Open Issues and Challenges of Green Practices in Social Commerce -- 2.12 The Practical Challenge for Greening Marketing Mix -- 2.13 Promotion of Green Marketing -- 3 Conclusion -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Sustainability of Marketing -- Social Responsibility in Marketing -- 1 Introduction -- 2 Marketing Definition -- 3 Corporate Social Responsibility (CSR), Definition and Background -- 4 Types of Corporate Social Responsibility -- 5 Corporate Social Responsibility in Marketing -- 6 CSR Implements in Marketing -- 7 Conclusion -- References -- Sustainability and Social Responsibility in Marketing -- 1 Introduction.
2 Literature Review -- 3 Corporate Social Responsibility in Marketing -- 4 Corporation Efforts in Communicating Their Corporate Social Responsibility Initiatives -- 5 Social Responsibility and Profitability -- 6 Conclusions and Recommendations -- References -- Artificial Neural Network and Structural Equation Modeling Techniques: Future Research Directions -- Artificial Neural Network and Structural Equation Modeling in the Future -- 1 Introduction -- 2 Exploring the Relations Between ANN and SEM -- 3 The Benefits of Structural Equation Modeling and Artificial Neural Network Approach -- 4 Artificial Neural Network and Structural Equation Modeling: Systematic Literature Review -- 5 Recommendations of the Previous Studies for SEM and ANN Analysis -- 6 Suggestions for Future Research -- 7 Conclusion -- References.
Record Nr. UNISA-996499865903316
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Assessment and data systems in early childhood settings : theory and practice / / edited by Claire McLachlan [and three others]
Assessment and data systems in early childhood settings : theory and practice / / edited by Claire McLachlan [and three others]
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer, , [2023]
Descrizione fisica 1 online resource (353 pages)
Disciplina 810.80355
Collana Early Childhood Research and Education: An Inter-theoretical Focus
Soggetto topico Education - Data processing
Educació infantil
Educació primària
Processament de dades
Tests i proves en educació
Soggetto genere / forma Llibres electrònics
ISBN 981-19-5959-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Using data systems to inform early childhood practice -- 2. Revisiting the roles of teachers as assessors of children’s progress -- 3. The Collection and Use of Assessment information in Early Childhood Settings -- 4. Using digital tools to support STEM learning -- 5. An exploration of how e-portfolios shape how learning is supported, evidenced and communicated in early years education -- 6. Tools and time for noticing in early childhood pedagogy outdoors -- 7. Documentation as a tool for changing practices in Iceland -- 8. Using new tools to support a data, knowledge, action stance to explore children’s experiences of curriculum -- 9. Using a variety of data collection methods to better understand students in physical education in primary schools -- 10. Disrupting the myths regarding young children’s vulnerabilities by assessing emotional and social wellbeing -- 11. Dual purposes: Using children’s self-assessment plans as summative data -- 12. Child-Voiced Assessment for Understanding Children’s Learning and Transforming Pedagogic Practices -- 13. Children as assessors of their own learning- the power of listening to children’s own reflections -- 14. Developing teachers’ capacity to use data systems -- 15. Using Activity-Focused Assessment Within an Embedded Instruction Framework -- 16. Towards the future use of data systems in early years settings. .
Record Nr. UNINA-9910637727903321
Singapore : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Basic data analysis for time series with R / / DeWayne R. Derryberry
Basic data analysis for time series with R / / DeWayne R. Derryberry
Autore Derryberry DeWayne R.
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (320 p.)
Disciplina 001.4/2202855133
Soggetto topico Time-series analysis - Data processing
R (Computer program language)
Anàlisi de sèries temporals
Processament de dades
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-118-59337-5
1-118-59323-5
1-118-59336-7
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? .
Record Nr. UNINA-9910132187103321
Derryberry DeWayne R.  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Basic data analysis for time series with R / / DeWayne R. Derryberry
Basic data analysis for time series with R / / DeWayne R. Derryberry
Autore Derryberry DeWayne R.
Edizione [1st edition]
Pubbl/distr/stampa Hoboken, New Jersey : , : Wiley, , 2014
Descrizione fisica 1 online resource (320 p.)
Disciplina 001.4/2202855133
Soggetto topico Time-series analysis - Data processing
R (Computer program language)
Anàlisi de sèries temporals
Processament de dades
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-118-59337-5
1-118-59323-5
1-118-59336-7
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Machine generated contents note: Part I - Basic correlation structures Chapter 0 - R basics 0.1 Getting started 0.2 Special R conventions 0.3 Common structures 0.4 Common functions 0.5 Time series functions 0.6 Importing data Chapter 1 - Review of regression and more about R 1.1 Goals of this chapter 1.2 The simple(st) regression model 1.3 Simulating the data from a model and estimating the model parameters in R 1.4 Basic inference for the model 1.5 Residuals analysis - What can go wrong... 1.6 Matrix manipulation in R Chapter 2 - The modeling approach taken in this book and some examples of typical serially correlated data 2.1 Signal and noise 2.2 Time series data 2.3 Simple regression in the framework 2.4 Real data and simulated data 2.5 The diversity of time series data 2.6 Getting data into R Chapter 3 - Some comments on assumptions 3.1 Introduction 3.2 The normality assumption 3.3 Equal variance 3.4 Independence 3.5 Power of logarithmic transformations illustrated 3.6 Summary Chapter 4 - The autocorrelation function and AR(1), AR(2) models 4.1 Standard models - What are the alternatives to white noise? 4.2 Autocovariance and autocorrelation 4.3 The acf() function in R 4.4 The first alternative to white noise: Autoregressive errors - AR(1), AR(2) Chapter 5 - The moving average models MA(1) and MA(2) 5.1 The moving average model 5.2 The autocorrelation for MA(1) models 5.3 A duality between MA(l) and AR(m) models 5.4 The autocorrelation for MA(2) models 5.5 Simulated examples of the MA(1) model 5.5 Simulated examples of the MA(2) model 5.6 AR(m) and MA(l) model acf() plots Part II - Analysis of periodic data and model selection Chapter 6 - Review of transcendental functions and complex numbers 6.1 Background 6.2 Complex arithmetic 6.3 Some important series 6.4 Useful facts about periodic transcendental functions Chapter 7 - The power spectrum and the periodogram 7.1 Introduction 7.2 A definition and a simplified form for p(f) 7.3 Inverting p(f) to recover the Ck values 7.4 The power spectrum for some familiar models 7.5 The periodogram, a closer look 7.6 The function spec.pgram() in R Chapter 8 - Smoothers, the bias-variance tradeoff, and the smoothed periodogram 8.1 Why is smoothing required? 8.2 Smoothing, bias, and variance 8.3 Smoothers used in R 8.4 Smoothing the periodogram for a series with a known period or unknown period. 8.5 Summary Chapter 9 - A regression model for periodic data. 9.1 The model 9.2 An example: the NYC temperature data 9.2 Complications 1: CO2 data 9.3 Complications 2: Sunspots 9.4 Complications 3: Accidental Deaths 9.5 Summary Chapter 10 - Basic model selection and cross validation. 10.1 Background 10.2 Hypothesis tests in simple regression 10.3 A more general setting for likelihood ratio tests 10.4 A subtlety different situation 10.5 Information criteria 10.6 Cross validation (Data splitting): NYC temperatures 10.7 Summary Chapter 11 - Fitting some Fourier series 11.1 Introduction: more complex periodic models 11.2 More complex periodic behavior: Accidental deaths 11.3 The Boise river flow data 11.4 Where do we go from here? Chapter 12 - Adjusting for AR(1) correlation in complex models 12.1 Introduction 12.2 The two sample t-test - Uncut and patch cut forest 12.3 The second Sleuth case - Global warming, a simple regression 12.4 The Semmelweis intervention 12.5 The NYC temperatures (adjusted) 12.6 The Boise river flow data: model selection with filtering 12.7 Implications of AR(1) adjustments and the "skip" method 12.8 Summary Part III - Complex temporal structures Chapter 13 - The backshift operator, the impulse response function, and general ARMA models 13.1 The general ARMA model 13.2 The backshift (shift, lag) operator 13.3 The impulse response operator - intuition 13.4 Impulse response operator, g(B) - computation 13.5 Interpretation and utility of the impulse response function Chapter 14 - The Yule-Walker equations and the partial autocorrelation function. 14.1 Background 14.2 Autocovariance of an ARMA(m,l) model 14.3 AR(m) and the Yule-Walker equations 14.4 The partial autocorrelation plot 14.5 The spectrum for ARMA processes 14.6 Summary Chapter 15 - Modeling philosophy and complete examples 15.1 Modeling overview 15.2 A complex periodic model - Monthly river flows, Furnas 1931-1978 15.3 A modeling example - trend and periodicity: CO2 levels at Mauna Lau 15.4 Modeling periodicity with a possible intervention - two examples 15.5 Periodic models: monthly, weekly, and daily averages 15.6 Summary Part IV - Some detailed and complete examples Chapter 16 - the Wolf sunspot number data 16.1 Background 16.2 Unknown period => nonlinear model 16.3 The function nls() in R 16.4 Determining the period 16.5 Instability in the mean, amplitude, and period 16.6 Data splitting for prediction 16.7 Summary Chapter 17 - Analysis of prostate and breast cancer data 17.1 Background 17.2 The first data set 17.3 The second data set Chapter 18 - Christopher Tennant/Ben Crosby watershed data 18.1 Background and question 18.2 Looking at the data and fitting Fourier series 18.3 Averaging data 18.4 Results Chapter 19 - Vostok ice core data 19.1 Source of the data 19.2 Background 19.3 Alignment 19.4 A naïve analysis 19.5 A related simulation 19.6 An AR(1) model for irregular spacing 19.7 Summary Appendices Appendix 1 - Using Data Market A1.1 Overview A1.2 Loading a time series in DataMarket A1.3 Respecting DataMarket licensing agreements Appendix 2 - AIC is PRESS A2.1 Introduction A2.2 PRESS A2.3 Connection to Akaike's result A2.4 Normalization and R2 A2.5 An example A2.6 Conclusion and further comments Appendix 3 - A 15 minute tutorial on optimization and nonlinear regression A3.1 Introduction A3.2 Newton's method for one dimensional nonlinear optimization A3.3 A direction, a step size, and a stopping rule A3.4 What could go wrong? A3.5 Generalizing the optimization problem A3.6 What could go wrong revisited A3.7 What can be done? .
Record Nr. UNINA-9910822358103321
Derryberry DeWayne R.  
Hoboken, New Jersey : , : Wiley, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann
Bayes Factors for Forensic Decision Analyses with R [[electronic resource] /] / by Silvia Bozza, Franco Taroni, Alex Biedermann
Autore Bozza Silvia
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham, : Springer Nature, 2022
Descrizione fisica 1 online resource (XII, 187 p. 22 illus., 5 illus. in color.)
Disciplina 519.5
Collana Springer Texts in Statistics
Soggetto topico Statistics
Mathematical statistics—Data processing
Forensic sciences
Medical jurisprudence
Forensic psychology
Social sciences—Statistical methods
Statistical Theory and Methods
Statistics and Computing
Forensic Science
Forensic Medicine
Forensic Psychology
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
Estadística bayesiana
Processament de dades
Criminalística
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Bayes factor
scientific evidence
decision making
forensic science
uncertainty management
probability theory
forensic
decision analysis
Bayesian modeling
R
Bayesian statistics
probabilistic inference
ISBN 3-031-09839-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Introduction to the Bayes factor and decision analysis -- Chapter 2: Bayes factor for model choice -- Chapter 3: Bayes factor for evaluative purposes -- Chapter 4: Bayes factor for investigative purposes.
Record Nr. UNISA-996495166503316
Bozza Silvia  
Cham, : Springer Nature, 2022
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui