Fabian LR

Based on the Cramér-Rao inequality (in the multiparameter case) the lower bound of Fisher information matrix is achieved if and only if the underlying distribution is ther-parameter exponential family. This family and the lower bound of Fisher information matrix are characterized when some constraints in the form of expected values of some statistics are available. If we combine the previous results we can find the class of parametric functions and the corresponding UMVU estimators via Cramér-Rao inequality.

Convergence rate results are derived for a stochastic optimization problem where a performance measure is minimized with respect to a vector parameter �. Assuming that a gradient estimator is available and that both the bias and the variance of the estimator are (known) functions of the budget devoted to its computation, the gradient estimator is employed in conjunction with a

Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use generic automatic parameter optimisation algorithms are known only for special problems such as the adjustment of the parameters of an evaluation function. The SPSA algorithm (Simultaneous Perturbation Stochastic Approximation) is a generic stochastic gradient method for optimising an objective function when an analytic expression of the gradient is not available, a frequent case in game programs. Further, SPSA in its canonical form is very easy to implement. As such, it is an attractive choice for parameter optimisation in game programs, both due to its generality and simplicity. The goal of this paper is twofold: (i) to introduce SPSA for the game programming community by putting it into a game-programming perspective, and (ii) to propose and discuss several methods that can be used to enhance the performance of SPSA. These methods include using common random numbers and antithetic variables, a combination of SPSA with RPROP, and the reuse of samples of previous performance evaluations. SPSA with the proposed enhancements was tested in some large-scale experiments on tuning the parameters of an opponent model, a policy and an evaluation function in our poker program, MCRAISE. Whilst SPSA with no enhancements failed to make progress using the allocated resources, SPSA with the enhancements proved to be competitive with other methods, including TD-learning; increasing the average payoff per game by as large as 0.19 times the size of the amount of the small bet. From the experimental study, we conclude that the use of an appropriately enhanced variant of SPSA for the optimisation of game program parameters is a viable approach, especially if no good alternative exist for the types of parameters considered.

Most of the current SAR systems aquire fully polarimetric data where the obtained scattering information can be represented by various coherent and incoherent parameters. In previous contributions we reviewed these parameters in terms of their "utility" for landcover classification, here, we investigate their impact on several classification algoritms. Three classifiers: the minimum-distance classifier, a multi-layer perceptron (MLP) and one based on logistic regression (LR) were applied on an L-Band scene acquired by the E-SAR sensor. MLP and LR were chosen because they are robust w.r.t. the data statistics. An interesting result is that MLP gives better results on the coherent parameters while LR gives better results on the incoherent parameters.

Objective Evaluation of the usefulness of criteria for systemic inflammatory response syndrome (SIRS) compared with the final diagnosis of infection in patients admitted to the emergency room of two university-based hospitals. Design Longitudinal cohort study. Setting Hospital Universitario San Vicente de Paul and Hospital General de Medellín, Medellín, Colombia. Patients Seven hundred thirty-four patients with suspected infection as main diagnosis for admittance into the emergency room. Measurements and results Sensitivity, specificity, predictive values and likelihood ratios (LR) of SIRS criteria at admission were determined using, as gold standards, the diagnosis at the time of discharge based on clinical history and evolution, and microbiological confirmation of infection. SIRS criteria were met by 503 patients (68.5%); the discharge diagnosis of infection was found in 657 (89.4%) and 276 (37%) had microbiological confirmation. SIRS criteria exhibited a sensitivity of 69%, specificity of 35%, positive predictive value (PPV) of 90%, negative predictive value (NPV) of 12% and positive LR of 1.06. There were no differences between the two gold standards. Conclusions The finding of two or more SIRS criteria was of little usefulness for diagnosis of infection. It is necessary to work with new criteria and probably with biological markers, in order to obtain a simple, precise and operative definition of the sepsis phenomenon.