Maker Inference

SUPPLEMENT TO GMM ESTIMATION AND UNIFORM SUBVECTOR INFERENCE WITH POSSIBLE IDENTIFICATION FAILURE By

Valued outranking relations in ELECTRE providing manageable disaggregation procedures

In ELECTRE methods, the construction of an outranking relation amounts at validating or invalidating, for any pair of alternatives (a; b), the assertion ‘‘a is at least as good as b’’. This comparison is grounded on the evaluation vectors of both alternatives, and on additional information concerning the decision maker s preferences, accounting for two conditions: concordance and non-discordanc...

Gmm Based Inference with Standard Stratified Samples When the Aggregate Shares Are Known

We show how to do efficient moment based inference using the generalized method of moments (GMM) when data is collected by standard stratified sampling and the maintained assumption is that the aggregate shares are known.

Pre and post break parameter inference

This paper discusses inference about the pre and post break value of a scalar parameter in GMM time series models with a single break at an unknown point in time. We show that treating the break date estimated by least squares as the true break date leads to substantially oversized tests and confidence intervals unless the break is large. We develop an alternative test that controls size unifor...

Using Fuzzy Gaussian Inference and Genetic Programming to Classify 3D Human Motions

This research introduces and builds on the concept of Fuzzy Gaussian Inference(FGI) [1][2] as a novel way to build Fuzzy Membership Functions that map to hidden Probability Distributions underlying human motions. This method is now combined with a Genetic Programming Fuzzy rule-based system in order to classify boxing moves from natural human Motion Capture data. In this experiment, FGI alone i...

Stochastic Annealing for Variational Inference

We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimiz...