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Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory.
The theory and practice of first-class prompts
Felleisen, Mattias · 1988
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Effects of copy-on-write memory management on the response time of UNIX fork operations
Smith, Jonathan M. and Maguire, Jr, Gerald Q · 1988
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SSA is functional programming
Appel, Andrew W · 1998
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Sequential Monte Carlo methods in practice
Doucet, Arnaud, De Freitas, Nando, Gordon, Neil, et al · 2001
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IBAL: A probabilistic rational programming language
Pfeffer, Avi · 2001
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IEEE Std 1003.1, 2004 Edition, 2004a
Open Group, The · 2004
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IEEE Std 1003.1, 2004 Edition, 2004b
Open Group, The · 2004
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Andrieu, Christophe, Doucet, Arnaud, and Holenstein, Roman · 2010
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Lightweight implementations of probabilistic programming languages via transformational compilation
Wingate, David, Stuhlmueller, Andreas, and Goodman, Noah D · 2011
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On the role of interaction in sequential Monte Carlo algorithms
Whiteley, Nick, Lee, Anthony, and Heine, Kari · 2013
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Venture: a higher-order probabilistic programming platform with programmable inference
Mansinghka, Vikash, Selsam, Daniel, and Perov, Yura · 2014
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A new approach to probabilistic programming inference
Wood, Frank, van de Meent, Jan Willem, and Mansinghka, Vikash · 2014
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