{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:26:52Z","timestamp":1740202012454,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"abstract":"<jats:p>Many applications that involve inference and learning in signal processing, communication and artificial intelligence can be cast into a graph framework. Factor graphs are a type of network that can be studied and solved by propagating belief messages with the sum\/product algorithm. In this paper we provide explicit matrix formulas for inference and learning in finite alphabet Forney-style factor graphs, with the precise intent of allowing rapid prototyping of arbitrary topologies in standard software like MATLAB.<\/jats:p>","DOI":"10.3233\/978-1-58603-984-4-154","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:27:05Z","timestamp":1740133625000},"source":"Crossref","is-referenced-by-count":0,"title":["Notes on Factor Graphs"],"prefix":"10.3233","author":[{"family":"Palmieri Francesco","sequence":"additional","affiliation":[]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Directions in Neural Networks"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:44:32Z","timestamp":1740134672000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=193&spage=154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-58603-984-4-154","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2009]]}}}