[1]	R.D. Nowak. Distributed EM algorithms for density estimation and clustering in sensor networks. IEEE Trans. Signal Processing, 51, August 2003.
[2]	J. Riba, J. Sala, and G. Vazquez. Conditional maximum likelihood timing recovery: Estimators and bounds. IEEE Trans. Signal Processing, 49:835-850, April 2001.
[3]	F.R. Kschischang, B.J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Trans. Inform. Theory, 47:498-519, February 2001.
[4]	Special Issue on Monte Carlo Methods for Statistical Signal Processing. IEEE Trans. Signal Processing, 50, February 2002.
[5]	M. Hürzeller and H.R. Künsch. Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7:175-193, 1998.
[6]	M. Coates. Distributed particle filters for sensor networks. In Third International Symposium on Information Processing in Sensor Networks, pages 99-107, Berkeley, CA, April 2004.
[7]	C. Guestrin, P. Bodik, R. Thibaux, M. Paskin, and S. Madden. Distributed regression: An efficient framework for modeling sensor network data. In Third International Symposium on Information Processing in Sensor Networks, pages 1-10, Berkeley, CA, April 2004.
[8]	M. Chu, H. Haussecker, and F. Zhao. Scalable information-driven sensor querying and routing for ad hoc heterogeneous sensor networks. International Journal of High Performance Computing Applications, 16:293-313, 2002.
[9]	J. Liu, J. Reich, and F. Zhao. Collaborative in-network processing for target tracking. EURASIP Journal on Applied Signal Processing, 4:378-391, 2003.
[10]	M. Rabbat, J. Haupt, A. Singh, and R. Nowak. Decentralized compression and predistribution via randomized gossiping. In Proc. 5th International Symposium on Information Processing in Sensor Networks, pages 51-59, Nashville, TN, April 2006.
[11]	M. Alanyali and V. Saligrama. Distributed tracking in multi-hop networks with communication delays and packet losses. In Proc. 13th Workshop on Statistical Signal Processing, pages 1190-1195, Bordeaux, France, July 2005.
[12]	H.A. Cirpan and M.K. Tsatsanis. Stochastic maximum likelihood methods for semi-blind channel estimation. IEEE Signal Processing Letters, 5:21-24, January 1998.
[13]	X.D. Wang and H.V. Poor. Iterative (turbo) soft interference cancellation and decoding for coded CDMA. IEEE Trans. Commun., 47:1046-1061, July 1999.
[14]	S.L. Scott. Bayesian methods for hidden Markov models: Recursive computing in the 21st century. J. Amer. Stat. Assoc., 97:337-351, March 2002.
[15]	Special Issue on Iterative, Soft Signal Processing for Digital Communications. IEEE Signal Processing Mag., 21, January 2004.
[16]	N. Noels, V. Lottici, A. Dejonghe, H. Steendam, M. Moeneclaey, M. Luise, and L. Vandendorpe. A theoretical framework for soft-information-based synchronization in iterative (turbo) receivers. J. Wireless Comm. Networking, 2:117-129, 2005.
[17]	J.S. Liu, A.F. Neuwald, and C.E. Lawrence. Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Amer. Stat. Assoc., 90:1156-1170, December 1995.
[18]	A.T. Ihler, J.W. Fisher, R.L. Moses, and A.S. Willsky. Nonparametric belief propagation for self-localization of sensor networks. IEEE J. Select. Areas Commun., 23:809-819, April 2005.
[19]	J.S. Liu and R. Chen. Blind deconvolution via sequential imputations. J. Amer. Stat. Assoc., 90:567-576, June 1995.
[20]	Y. Sung, S. Misra, L. Tong, and A. Ephremides. Cooperative routing for distributed detection in large sensor networks. IEEE J. Select. Areas Commun., 25:471-483, February 2007.
[21]	S. Aeron, M. Zhao, and V. Saligrama. Fundamental tradeoffs between sparsity, sensing diversity and sensing capacity. In Proc. 40th Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, November 2006.
[22]	J.-F. Chamberland and V.V. Veeravalli. Decentralized detection in sensor networks. IEEE Trans. Signal Processing, 51, February 2003.
[23]	T. Michaeli and Y.C. Eldar. Minimum MSE estimation with convex constraints. In Proc. Int. Conf. Acoust., Speech, Signal Process., pages 1093-1096, Honolulu, HI, April 2007.