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L_approximation Reference

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7 documents available in our comprehensive collection of L_approximation Reference resources. Find practical guides, tutorials, and documentation to enhance your knowledge.

Preview of Stochastic Planning Inference
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Stochastic Planning Inference (wu22a.pdf)

548 KBEN 12 pages
Stochastic planning is reduced to probabilistic inference in large discrete graphical models, requiring approximation schemes due to hardness of inference.
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Preview of Distortion Estimates
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Distortion Estimates (496_main_paper.pdf)

760 KBEN 10 pages
Distortion estimates for approximate Bayesian inference are crucial for evaluating posterior approximation schemes. A "distortion map" is estimated to adjust univariate marginals of the approximate posterior. This approach provides graphical diagnostics at the observed data.
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Preview of AD7416 Datasheet
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AD7416 Datasheet (AD7417.pdf)

523 KBAnalog Devices, Inc.EN 24 pages
The AD7416, AD7417, and AD7418 are 10-bit digital temperature sensors and ADCs with on-chip temperature sensors, operating from a 2.7V to 5.5V power supply,...
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Preview of MAX1165.pdf
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MAX1165.pdf (MAX1165.pdf)

177 KBKaren HeaneyEN 15 pages
The MAX1165/MAX1166 are 16-bit, low-power, successive-approximation analog-to-digital converters (ADCs) with automatic power-down, factory-trimmed internal...
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Preview of RStan Package
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RStan Package (rstan.pdf)

330 KBJiqiang Guo; Jonah Gabry; Ben Goodrich; Andrew Johnson; Sebastian Weber; Hamada S. BadrEN 82 pages
R package 'rstan' provides interface to Stan, a probabilistic programming language. It enables Bayesian statistical inference via Markov Chain Monte Carlo and other methods. Version 2.32.7 is available.
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Preview of FY2022 Research Results(PDF 1.81MB)
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FY2022 Research Results(PDF 1.81MB) (20230328symposium_10_poster.pdf)

1.77 MBhtakagiEN 1 page
The Approximate Bayesian Inference Team, led by Mohammad Emtiyaz Khan, aims to develop AI systems that can learn and improve continually throughout their...
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Preview of Proving Bounds
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Proving Bounds (IRIT-RR-2014-09-FR.pdf)

310 KBEN 32 pages
Proving tight bounds on univariate expressions in Coq. A tactic for the Coq proof assistant is presented to automatically prove bounds. Formal proof of numerical bounds on approximation errors is achieved.
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