Above are papers related to the following paper: [Paper_ID] Paper #44121. 	[Title] Factor Graphs and Algorithms [Abstract] A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a general algorithm for computing "marginals" of the global function by distributed message-passing in the corresponding factor graph. A wide variety of algorithms developed in the artificial intelligence, statistics, signal processing, and digital communications communities can be derived as specific instances of this general algorithm, including Pearl's "belief propagation" and "belief revision" algorithms, the fast Fourier transform, the Viterbi algorithm, the forward/backward algorithm, and the iterative "turbo" decoding algorithm. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104435. 	[Title] Trellis-Constrained Codes [Abstract] We introduce a class of iteratively decodable trellis-constrained codes as a generalization of turbocodes, low-density parity-check codes, serially-concatenated convolutional codes, and product codes. In a trellis-constrained code, multiple trellises interact to define the allowed set of codewords. As a result of these interactions, the minimum-complexity single trellis for the code can have a state space that grows exponentially with block length. However, as with turbocodes and low-density parity-check codes, a decoder can approximate bit-wise maximum a posteriori decoding by using the sum-product algorithm on the factor graph that describes the code. We present two new families of codes, homogenous trellis-constrained codes and ring-connected trellis-constrained codes, and give results that show these codes perform in the same regime as do turbo-codes and low-density parity-check codes. Please summarise all the given information.

Paper #44121 has the title `Factor Graphs and Algorithms  
` and the abstract `A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a general algorithm for computing "marginals" of the global function by distributed message-passing in the corresponding factor graph. A wide variety of algorithms developed in the artificial intelligence, statistics, signal processing, and digital communications communities can be derived as specific instances of this general algorithm, including Pearl's "belief propagation" and "belief revision" algorithms, the fast Fourier transform, the Viterbi algorithm, the forward/backward algorithm, and the iterative "turbo" decoding algorithm. 
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Question: Which category should Paper #44121 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #44121 has the title `Factor Graphs and Algorithms  
` and the abstract `A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a general algorithm for computing "marginals" of the global function by distributed message-passing in the corresponding factor graph. A wide variety of algorithms developed in the artificial intelligence, statistics, signal processing, and digital communications communities can be derived as specific instances of this general algorithm, including Pearl's "belief propagation" and "belief revision" algorithms, the fast Fourier transform, the Viterbi algorithm, the forward/backward algorithm, and the iterative "turbo" decoding algorithm. 
`.
Question: Which category should Paper #44121 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods