We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. A probabilistic representation is used for al

适用于空间计量的各种模型，包括SVAR SEM SMD 等，以及各种检验，如LM Walds等-least-squares, simultaneous systems (2SLS,3SLS, SUR) limited dependent variable (logit, probit, tobit) and Bayesian variants time-series (VAR, BVAR, ECM) estimation and accompanying forecasting func