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    https://sophisticatedspectra.com/article/drosia-serenity-a-modern-oasis-in-the-heart-of-larnaca.2521391.html

    DROSIA SERENITY
    A Premium Residential Project in the Heart of Drosia, Larnaca

    ONLY TWO FLATS REMAIN!

    Modern and impressive architectural design with high-quality finishes Spacious 2-bedroom apartments with two verandas and smart layouts Penthouse units with private rooftop gardens of up to 63 m² Private covered parking for each apartment Exceptionally quiet location just 5–8 minutes from the marina, Finikoudes Beach, Metropolis Mall, and city center Quick access to all major routes and the highway Boutique-style building with only 8 apartments High-spec technical features including A/C provisions, solar water heater, and photovoltaic system setup.
    Drosia Serenity is not only an architectural gem but also a highly attractive investment opportunity. Located in the desirable residential area of Drosia, Larnaca, this modern development offers 5–7% annual rental yield, making it an ideal choice for investors seeking stable and lucrative returns in Cyprus' dynamic real estate market. Feel free to check the location on Google Maps.
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    Bayesian Statistical Modelling (repost)

    Posted By: arundhati
    Bayesian Statistical Modelling (repost)

    Peter Congdon, "Bayesian Statistical Modelling"
    2007 | ISBN: 0470018755 | 596 pages | PDF | 3 MB

    Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.

    Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.

    The second edition:
    - Provides an integrated presentation of theory, examples, applications and computer algorithms.
    - Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
    - Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
    - Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
    - Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.

    Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.