Learning in Graphical Models
M.I. Jordan
Π΄Π΅ΠΊ. 2012β―Π³. Β· Springer Science & Business Media
Π-ΠΊΠ½ΠΈΠ³Π°
630
Π‘ΡΡΠ°Π½ΠΈΡΠΈ
reportΠΡΠ΅Π½ΠΈΡΠ΅ ΠΈ ΡΠ΅ΡΠ΅Π½Π·ΠΈΠΈΡΠ΅ Π½Π΅ ΡΠ΅ ΠΏΠΎΡΠ²ΡΠ΄Π΅Π½ΠΈ Β ΠΠΎΠ·Π½Π°ΡΡΠ΅ ΠΏΠΎΠ²Π΅ΡΠ΅
ΠΠ° Π΅-ΠΊΠ½ΠΈΠ³Π°Π²Π°
In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume.
Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail.
Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail.
Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
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ΠΠ° Π΄Π° ΡΠΈΡΠ°ΡΠ΅ Π½Π° ΡΡΠ΅Π΄ΠΈ ΡΠΎ Π΅-ΠΌΠ°ΡΡΠΈΠ»ΠΎ, ΠΊΠ°ΠΊΠΎ ΡΡΠΎ ΡΠ΅ Π΅-ΡΠΈΡΠ°ΡΠΈΡΠ΅ Kobo, ΡΠ΅ ΡΡΠ΅Π±Π° Π΄Π° ΠΏΡΠ΅Π·Π΅ΠΌΠ΅ΡΠ΅ Π΄Π°ΡΠΎΡΠ΅ΠΊΠ° ΠΈ Π΄Π° ΡΠ° ΠΏΡΠ΅ΡΡΠ»ΠΈΡΠ΅ Π½Π° ΡΡΠ΅Π΄ΠΎΡ. Π‘Π»Π΅Π΄Π΅ΡΠ΅ Π³ΠΈ Π΄Π΅ΡΠ°Π»Π½ΠΈΡΠ΅ ΡΠΏΠ°ΡΡΡΠ²Π° Π²ΠΎ Π¦Π΅Π½ΡΠ°ΡΠΎΡ Π·Π° ΠΏΠΎΠΌΠΎΡ Π·Π° ΠΏΡΠ΅ΡΡΠ»Π°ΡΠ΅ Π½Π° Π΄Π°ΡΠΎΡΠ΅ΠΊΠΈΡΠ΅ Π½Π° ΠΏΠΎΠ΄Π΄ΡΠΆΠ°Π½ΠΈ Π΅-ΡΠΈΡΠ°ΡΠΈ.
