Hola tod@s. Este Viernes tenemos la primera reunión de los seminarios del Grupo de Termodinámica y Computació Cuántica. Este primer seminario será impartido por Michalis Skotiniotis (titulo y resumen a continuación) a las 12:00 en el Laboratorio de Física Computacional situada en el departamento de Electromagnetismo y Física Materia (planta baja circa de péndulo gigante). La charla será accesible para todos, por lo que se recomienda encarecidamente a los estudiantes de pregrado que se unan. Para facilitar la comunicación, también hemos revivido la pagina web del club de revistas cuánticas que se puede encontrar aquí https://ic1.ugr.es/eventos/wp/qjc/. Marque la pagina web como favorito, ya que lo usaremos para cargar anuncios de seminarios, así como material de audio, visual, y textual para los seminarios. Nos vemos a todos allí DJM —————————————————————————————————————————————————————————————————————————————— Dear all. This Friday we will have thee first seminar of the Quantum Thermodynamics and Computation Group. The seminar will be given by Michalis Skotiniotis (title and abstract below) at 12:00 in the Computational Physics laboratory in the Department of Electromagnetism and Material Physics (ground floor next to the giant pendulum). The level of the seminar will be such so as to be accessible to all, particularly students of the undergrad degree interested in the topic whom we strongly encourage to attend. To facilitate future communication, we have also revived the webpage of the quantum journal club which can be found here https://ic1.ugr.es/eventos/wp/qjc/. Please bookmark this webpage as we will use it to announce future seminars, as well as upload audio, visual, and textual material pertaining to the seminars. See you all there DJM. —————————————————————————————————————————————————————————————————————————————— Title: Classical Statistical Inference—Hypothesis Testing and Parameter Estimation Speaker: Michalis Skotiniotis Abstract: Statistical inference is an important branch of decision theory and deals with taking optimal decisions in the face of uncertainty. For instance, the decision of whether a new drug combats a given disease or not is taken based on samples taken from medical trials and forms the primary example of classical hypothesis testing. On the other hand, determining the precision in the estimation of a certain physical constant—such as the value of the gravitational constant g—is an example of parameter estimation. In this talk we will go through both these tasks from the point of view of statistical decision theory. In the case of hypothesis testing we will derive the optimal decision rule for minimizing the probability of erroneously identifying the likely hypothesis, as well as several variants of symmetric and asymmetric hypothesis testing (the famous Neyman-Pearson theorem). For parameter estimation we will derive the celebrated Cramér-Rao theorem which sets a lower bound on the precision of any unbiased estimator, and we will connect this to the traditional error propagation formula we all know and love (or hate as the case may be).