IV Intervencao do Curso de Arquitetura e Urbanismo | Oficina IOcorreu entre os dias 07 e 12 de Junho no Centro Cultural da Universidade Federal de Sao Joao del Rei.A foto mostra o trabalho do grupo formado por Driely, Juliana, Yuri, Ricardo e Rayan.Trabalho esse que levou o nome de Ascensao. Estou tendo a oportunidade de participar de uma oficina com os Gambiologos, um grupo de designers e artistas que lidam com a ciencia do improviso aliada as tecnicas eletronico-digitais. Ja estou muito empolgado e tenho muitas expectativas quanto ao aprendizado. Gostei muito da apresentacao da ementa que eles fizeram e acho que vou gostar muito de passar por essa oficina. Desenho feito por mim para um trabalho sobre a Mesopotamia da Materia da Historia da Arquitetura e Urbanismo Montagem: Rayan Andrew Gelman: discussion of a recent article by David Spiegelhalter, ChristopherSherlaw-Johnson, Martin Bardsley, Ian Blunt, Christopher Wood and Olivia Grigg, that is scheduled to appear in the Journal of the Royal Statistical Society:Finally, I am glad that these methods result in ratings rather than rankings. As has been discussed by Louis (1984), Lockwood et al. (2002), and others, two huge problems arise when constructing ranks from noisy data. First, with unbalanced data (for example, different sample sizes in different hospitals) there is no way to simultaneously get reasonable point estimates of parameters and their rankings. Second, ranks are notoriously noisy. Even with moderately large samples, estimated ranks are unstable and can be misleading, violating well-known principles of quality control by encouraging decision makers to chase noise rather than understanding and reducing variation (Deming, 2000). Thus, although I am unhappy with the components of the methods being used here, I like some aspects of the output.Extraido do blog: Statistical Modeling, Causal Inference, and Social Science In order to use mathematical modelling in order to solve a real-world problem, one ideally would like to have three ingredients besides the actual mathematical analysis:* A good mathematical model. This is a mathematical construct which connects the observable data, the predicted outcome, and various unspecified parameters of the model to each other. In some cases, the model may be probabilistic instead of deterministic (thus the predicted outcome will be given as a random variable rather than as a fixed quantity).* A good set of observable data.* Good values for the parameters of the model.For instance, if one wanted to work out the distance D to a distant galaxy, the model might be Hubble's law v = H D relating the distance to the recessional velocity v, the data might be the recessional velocity v (or, more realistically, a proxy for that velocity, such as the red shift), and the only parameter in this case would be the Hubble constant H. This is a particularly simple situation; of course, in general one would expect a much more complex model, a much larger set of data, and a large number of parameters. (And parameters need not be numerical; a model, for instance, could posit an unknown functional relationship between two observable quantities, in which case the function itself