ALBERT

Note: Contents: 1. Introduction. 2. RC Filter. 3. General linear time invariant systems. 4. The seismometer. 5. The sampling process. 6. Analog-to-digital conversion. 7. From infinitely continuous to finite discrete. 8. The digital anti-alias filter. 9. Inverse and simulation filtering of digital seismograms. 10. The measurement of wavelet parameters from digital seismograms. References. Appendix: Solution to problems.

Note: TABLE OF CONTENTS: Preface / R. Matzke-Karasz, M. Schudack, K. Martens. - REVIEW PAPER. - Ostracod recovery in the aftermath of the Permian-Triassic crisis: Palaeozoic-Mesozoic turnover / S. Crasquin-Soleau, T. Galfetti, H. Bucher, S. Kershaw, Q. Feng. - OSTRACOD TAXONOMY AND BIOGEOGRAPHY. - The influence of El Niño 1997-98 on pelagic ostracods in the Humboldt Current Ecosystem off Peru / R. Castillo, T. Antezana, P. Ayón. - A new, interstitial species of Terrestricythere (Crustacea: Ostracoda) and its microdistribution at Orito Beach, northeastern Sea of Japan / S. F. Hiruta, S.-i. Hiruta, S. F. Mawatari. - Non-marine Ostracoda (Crustacea) of Banat district in Serbia / T. Karan-Žnidaršič, B. Petrov. - ECOLOGICAL FACTORS AFFECTING OSTRACOD DISTRIBUTION. - A year round comparative study on the population structures of pelagic Ostracoda in Admiralty Bay (Southern Ocean) / K. Blachowiak-Samolyk, M.V. Angel. - Water quality and diversity of the Recent ostracod fauna in lowland springs from Lombardy (northern Italy) / V. Pieri, C. Caserini, S. Gomarasca, K. Martens, G. Rossetti. - Factors affecting spatial and temporal distribution of Ostracoda assemblages in different macrophyte habitats of a shallow lake (Lake Fehér, Hungary) / A. Kiss. - Groundwater Ostracods from the arid Pilbara region of northwestern Australia: distribution and water chemistry / J. M. Reeves, P. De Deckker, S. A. Halse. - Ecological requirements of Ostracoda (Crustacea) in a heavily polluted shallow lake, Lake Yeniçağa (Bolu, Turkey) / O. Külköylüoğlu, M. Dügel, M. Kılıç. - Food selection in Eucypris virens (Crustacea: Ostracoda) under experimental conditions / O. Schmit, G. Rossetti, J. Vandekerkhove, F. Mezquita. - EVOLUTIONARY SIGNIFICANCE OF OSTRACOD MORPHOLOGY. - Extra-lobal and complex dimorphic features in Middle Devonian palaeocopine ostracods / G. Becker, W. K. Braun. - Evolutionary and taxonomic aspects within the species group Pseudocandona eremita (Vejdovský) (Ostracoda, Candonidae) / S. lepure, T. Namiotko, D. L. Danielopol. - On the origin of the putative furca of the Ostracoda (Crustacea) / C. Meisch. - Ultrastructure of the carapace margin in the Ostracoda (Arthropoda: Crustacea) / S. Yamada. - Ultrastructure of hepatopancreas and its possible role as a hematopoietic organ in non-marine cypridoidean ostracods (Crustacea) / R. Symonova. - OSTRACOD REPRODUCTION AND ONTOGENY. - Copulatory behaviour and sexual morphology of three Fabaeformiscandona Krstić, 1972 (Candoninae, Ostracoda, Crustacea) species from Japan, including descriptions of two new species / R. J. Smith, T. Kamiya. - Early release of eggs and embryos in a brooding ancient asexual ostracod: brood selection or a gambling strategy to increase fecundity? / R. L. Pinto, C. E. F. Rocha, K. Martens. - The ontogeny of appendages of Heterocypris salina (Brady, 1868) Ostracoda (Crustacea) / N. Kubanç, O. Özuluğ, C. Kubanç.

Note: Translated from the Japanese

Note: Contents: 1 Introduction and examples. - 1.1 Introduction. - 1.2 Why Bayes?. - 1.2.1 Estimating the probability of a rare event. - 1.2.2 Building a predictive model. - 1.3 Where we are going. - 1.4 Discussion and further references. - 2 Belief, probability and exchangeability. - 2.1 Belief functions and probabilities. - 2.2 Events, partitions and Bayes' rule. - 2.3 Independence. - 2.4 Random variables. - 2.4.1 Discrete random variables. - 2.4.2 Continuous random variables. - 2.4.3 Descriptions of distributions. - 2.5 Joint distributions. - 2.6 Independent random variables. - 2.7 Exchangeability. - 2.8 de Finetti's theorem. - 2.9 Discussion and further references. - 3 One-parameter models. - 3.1 The binomial model. - 3.1.1 Inference for exchangeable binary data. - 3.1.2 Confidence regions. - 3.2 The Poisson model. - 3.2.1 Posterior inference . - 3.2.2 Example: Birth rates. - 3.3 Exponential families and conjugate priors. - 3.4 Discussion and further references. - 4 Monte Carlo approximation. - 4.1 The Monte Carlo method. - 4.2 Posterior inference for arbitrary functions. - 4.3 Sampling from predictive distributions. - 4.4 Posterior predictive model checking. - 4.5 Discussion and further references. - 5 The normal model. - 5.1 The normal model. - 5.2 Inference for the mean, conditional on the variance. - 5.3 Joint inference for the mean and variance. - 5.4 Bias, variance and mean squared error. - 5.5 Prior specification based on expectations. - 5.6 The normal model for non-normal data. - 5.7 Discussion and further references. - 6 Posterior approximation with the Gibbs sampler. - 6.1 A semiconjugate prior distribution. - 6.2 Discrete approximations. - 6.3 Sampling from the conditional distributions. - 6.4 Gibbs sampling. - 6.5 General properties of the Gibbs sampler. - 6.6 Introduction to MCMC diagnostics. - 6.7 Discussion and further references. - 7 The multivariate normal model. - 7.1 The multivariate normal density. - 7.2 A semiconjugate prior distribution for the mean. - 7.3 The inverse-Wishart distribution. - 7.4 Gibbs sampling of the mean and covariance. - 7.5 Missing data and imputation. - 7.6 Discussion and further references. - 8 Group comparisons and hierarchical modeling. - 8.1 Comparing two groups. - 8.2 Comparing multiple groups. - 8.2.1 Exchangeability and hierarchical models. - 8.3 The hierarchical normal model. - 8.3.1 Posterior inference. - 8.4 Example: Math scores in U.S. public schools. - 8.4.1 Prior distributions and posterior approximation. - 8.4.2 Posterior summaries and shrinkage. - 8.5 Hierarchical modeling of means and variances. - 8.5.1 Analysis of math score data. - 8.6 Discussion and further references. - 9 Linear regression. - 9.1 The linear regression model. - 9.1.1 Least squares estimation for the oxygen uptake data. - 9.2 Bayesian estimation for a regression model. - 9.2.1 A semiconjugate prior distribution. - 9.2.2 Default and weakly informative prior distributions. - 9.3 Model selection. - 9.3.1 Bayesian model comparison. - 9.3.2 Gibbs sampling and model averaging. - 9.4 Discussion and further references. - 10 Nonconjugate priors and Metropolis-Hastings algorithms. - 10.1 Generalized linear models. - 10.2 The Metropolis algorithm. - 10.3 The Metropolis algorithm for Poisson regression. - 10.4 Metropolis, Metropolis-Hastings and Gibbs. - 10.4.1 The Metropolis-Hastings algorithm. - 10.4.2 Why does the Metropolis-Hastings algorithm work?. - 10.5 Combining the Metropolis and Gibbs algorithms. - 10.5.1 A regression model with correlated errors. - 10.5.2 Analysis of the ice core data. -

Note: Contents: Part 1: Introduction ; The Global Adaptation Community Expands Its Scope ; Part 2: Urban Risk and Assessing Vulnerability at the Local Level ; Introduction: Urban Risk and Assessing Vulnerability at the Local Level ; A Region at Risk: Policy Determination Through Vulnerability Hotspot Assessment ; Developing a Framework for Assessing Coastal Vulnerability to Sea Level Rise in Southern New England, USA ; Quantifying Impacts of Potential Sea-Level Rise Scenarios on Irish Coastal Cities ; Mapping Risk and Vulnerability in São Paulo Metropolitan Region ; Water Crisis: Public Management of a Critical Situation ; Environmental Assessment and Restoration of Typhoon Morakot Disaster: A Case Study in Kaohsiung, Chinese Taipei ; Flood Risk Protection Concept for the Urban Region Geising/Altenberg in the Flood Formation Area of the Eastern Ore Mountains, Germany ; Part 3: Toward the Resilient City ; Introduction: Toward the Resilient City ; Water, Energy and Food Security in Mexico City ; Nature at the Heart of Urban Design for Resilience ; Smart City: Energy Efficiency in a New Scope ; Resilient Food Systems for Resilient Cities ; Urban Agriculture Casablanca ; Adapting Cities to Climate Change: Scenarios for Urban Neighbourhoods in the City of Essen ; Combining Urban Development with Climate Change Adaptation Using a Systems Approach ; Towards Resilient Architecture ; Towards More Resilient Water Infrastructures ; Building ‘Equitable’ Urban Resilience: The Challenge for Cities ; Climate Change and the Urban Poor: Support of the German Development Cooperation to a City in Bangladesh ; The Green Infrastructure Transect: An Organizational Framework for Mainstreaming Adaptation Planning Policies ; Part 4: Frameworks for Local Response to Climate Change: Challenges and Recommendations ; Introduction: Framework for Local Responses to Climate Change: Challenges and Recommendations ; Building Resilience in Asian Cities ; A Science-Policy Approach Towards Local Adaptation Planning: The Case of Santiago de Chile ; Participatory Action Adaptation: Tools for Increasing Climate Change Capacity and Preparedness at the Local Government Level ; Knowledge and Information for Resilient Cities ; Climate Change Guidelines for Urban Planning in the Basque Country ; Integrated Roadmapping to Shape Adaptation Processes in Metropolitan Areas ; The Significance of Adaptation Framing in Local and Regional Climate Change Adaptation Initiatives in Australia ; Decision-Making Frameworks for Adaptation to Extremes in Two Local Government Areas: Comparing and Contrasting India and Australia ; Urban Climate Governance in the Philippines, Mexico and South Africa: National- and State-Level Laws and Policies ; Space for Adapting: Reconciling Adaptation and Mitigation in Local Climate Change Plans ; The Early Experiences of Local Climate Change Adaptation in Norway Compared with That of Local Agenda 21 ; Climate Change Adaptation Plan of Vitoria-Gasteiz, Spain ; Integrated Climate Action: Linking Mitigation and Adaptation to Make Indonesian Cities Resilient ; Enhancing the Climate Change Adaptation Capabilities of Local Governments in Korea: Supporting Programs for Local Adaptation Plan ; Reality Check: Ho Chi Minh City, Vietnam ; Part 5: Financing the Resilient City ; Introduction: Financing the Resilient City ; A Demand-Driven Approach to Development, Disaster Risk Reduction, and Climate Adaptation ; Smarter Interventions in an Age of Uncertainty ; Linking Resilience and Green Growth: How Green Business Can Contribute to More Resilient Cities in India ; Green Areas Inner-City Agreement (GAIA): How Local Enterprises Can Contribute to Local Adaptation to Climate Change ; Financing Climate Change Adaptation: The Copenhagen Case ; Challenges on the Way to Financing Urban Climate Change Adaptation