Electrical Engineering and Computer Science, Department of (EECS)
http://hdl.handle.net/2027.42/60937
2016-07-25T23:59:15ZScenic bilevel image similarity metrics MATLAB code
http://hdl.handle.net/2027.42/122736
Scenic bilevel image similarity metrics MATLAB code
Zhai, Yuanhao
This item contains MATLAB code for scenic bilevel image similarity metrics described in the following two papers: (1) Y. Zhai and D.L. Neuhoff, Similarity of Scenic Bilevel Images, to appear in IEEE Transaction on Image Processing, 2016.
(2) Y. Zhai, D.L. Neuhoff and T.N. Pappas, Objective Similarity Metrics for Scenic Bilevel Images, IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), pp. 2793-2797, Florence, Italy, May 2014.
2016-07-24T00:00:00ZAn Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets (Online Appendix)
http://hdl.handle.net/2027.42/117580
An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets (Online Appendix)
Wah, Elaine; Lahaie, Sebastien; Pennock, David M.
Online appendix to accompany article published in 25th International Joint Conference on Artificial Intelligence (IJCAI-16)
2016-04-19T00:00:00ZMinimum Conditional Description Length Estimation for Markov Random Fields
http://hdl.handle.net/2027.42/117383
Minimum Conditional Description Length Estimation for Markov Random Fields
Reyes, Matthew; Neuhoff, David
In this paper we discuss a method, which we call Minimum Conditional Description Length (MCDL), for estimating the parameters of a subset of sites within a Markov random field. We assume that the edges are known for the entire graph $G=(V,E)$. Then, for a subset $U\subset V$, we estimate the parameters for nodes and edges in $U$ as well as for edges incident to a node in $U$, by finding the exponential parameter for that subset that yields the best compression conditioned on the values on the boundary $\partial U$. Our estimate is derived from a temporally stationary sequence of observations on the set $U$. We discuss how this method can also be applied to estimate a spatially invariant parameter from a single configuration, and in so doing, derive the Maximum Pseudo-Likelihood (MPL) estimate.
2016-02-23T00:00:00ZCutset Width and Spacing for Reduced Cutset Coding of Markov Random Fields
http://hdl.handle.net/2027.42/117382
Cutset Width and Spacing for Reduced Cutset Coding of Markov Random Fields
Reyes, Matthew; Neuhoff, David
In this paper we explore tradeoffs, regarding coding performance, between the thickness and spacing of the cutset used in Reduced Cutset Coding (RCC) of a Markov random field image model \cite{reyes2010}. Considering MRF models on a square lattice of sites, we show that under a stationarity condition, increasing the thickness of the cutset reduces coding rate for the cutset, increasing the spacing between components of the cutset increases the coding rate of the non-cutset pixels, though the coding rate of the latter is always strictly less than that of the former. We show that the redundancy of RCC can be decomposed into two terms, a correlation redundancy due to coding the components of the cutset independently, and a distribution redundancy due to coding the cutset as a reduced MRF. We provide analysis of these two sources of redundancy. We present results from numerical simulations with a homogeneous Ising model that bear out the analytical results. We also present a consistent estimation algorithm for the moment-matching reduced MRF for the cutset.
2016-02-23T00:00:00Z