Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Articles on Stochastic Optimization, Including: Multi-Armed Bandit, Neyer D-Optimal Test, Response Surface Methodology, Haybittle "Peto Boundary, Poco

Articles on Stochastic Optimization, Including: Multi-Armed Bandit, Neyer D-Optimal Test, Response Surface Methodology, Haybittle "Peto Boundary, Poco

Hephaestus Books

Paperback

General MathematicsGeneral Sociology

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ISBN10: 1244472123
ISBN13: 9781244472129
Publisher: Hephaestus Books
Pages: 66
Weight: 0.30
Height: 0.14 Width: 7.44 Depth: 9.69
Language: English
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Hephaestus Books represents a new publishing paradigm, allowing disparate content sources to be curated into cohesive, relevant, and informative books. To date, this content has been curated from Wikipedia articles and images under Creative Commons licensing, although as Hephaestus Books continues to increase in scope and dimension, more licensed and public domain content is being added. We believe books such as this represent a new and exciting lexicon in the sharing of human knowledge. This particular book contains chapters focused on Stochastic optimization, and Sequential experiments. More info: Stochastic optimization (SO) methods are optimization algorithms which incorporate probabilistic (random) elements, either in the problem data (the objective function, the constraints, etc.), or in the algorithm itself (through random parameter values, random choices, etc.), or in both . The concept contrasts with the deterministic optimization methods, where the values of the objective function are assumed to be exact, and the computation is completely determined by the values sampled so far.

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