Sampling (computational modeling) - Revision history

Pppery: ←Changed redirect target from Computer experimen#Sampling strategies to Computer experiment#Sampling strategies

2024-02-22T03:37:50Z

←Changed redirect target from Computer experimen#Sampling strategies to Computer experiment#Sampling strategies

← Previous revision		Revision as of 03:37, 22 February 2024
Line 1:		Line 1:
	#Redirect [[~~Computer_experimen~~#Sampling_strategies]]		#Redirect [[Computer_experiment#Sampling_strategies]]

Pppery: ←Changed redirect target from Computer experiment to Computer experimen#Sampling strategies

2024-02-22T03:37:46Z

←Changed redirect target from Computer experiment to Computer experimen#Sampling strategies

← Previous revision		Revision as of 03:37, 22 February 2024
Line 1:		Line 1:
	#Redirect [[~~Computer_experiment]] > [[Computer_experiment~~#Sampling_strategies~~\|Sampling strategies~~]]		#Redirect [[Computer_experimen#Sampling_strategies]]

Kozlova Mariia: Redirected to a more appropriate place

2024-02-21T09:44:51Z

Redirected to a more appropriate place

← Previous revision		Revision as of 09:44, 21 February 2024
Line 1:		Line 1:
	#Redirect [[Sampling ~~(statistics)~~]]		#Redirect [[Computer_experiment]] > [[Computer_experiment#Sampling_strategies\|Sampling strategies]]

MrOllie: Reverted 1 edit by Saltean (talk): Clearly a duplicate

2024-02-13T13:38:00Z

Reverted 1 edit by Saltean (talk): Clearly a duplicate

← Previous revision		Revision as of 13:38, 13 February 2024
Line 1:		Line 1:
		⚫	#Redirect [[Sampling (statistics)]]
	{{Orphan\|date=September 2023}}

	In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

	==Methods==
	The sampling strategies include different techniques.
	# [[Simple random sampling]] is the most common method used for Monte Carlo simulation.<ref>Olken, F., and Rotem, D. (1986). "Simple random sampling from relational databases."</ref> Simple random sampling can be implemented in any programming language and spreadsheet environment with a straightforward {{smallcaps\|rand}} function or its variations.
	# [[Latin hypercube sampling]] is a simple approach that attempts to improve coverage of the multidimensional space compared to simple random sampling but does not always outperform it.<ref>{{cite book \| last1 = Fang \| first1 = K. T. \| last2 = Li
	\| first2 = R. \| last3 = Sudjianto
	\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
	# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
	\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
	\|author2=Dutang, C. \|author3=Savicky, P.
	\|author4=Wuertz, D. \|author5=Knuth, D.
	\|author6=Matsumoto, M. \|author7=Saito, M. \|year=2023 }}</ref> Julia,<ref>{{cite web
	\|title=The QMC module for Julia
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

	==See also==
⚫	*[[Sampling (statistics)]]

	==References==
	{{reflist}}

	[[Category:Models of computation]]
	[[Category:Sampling (statistics)]]

Saltean: Undid revision 1206541598 by MrOllie (talk) - Please see talk page

2024-02-13T11:54:27Z

Undid revision 1206541598 by MrOllie (talk) - Please see talk page

← Previous revision		Revision as of 11:54, 13 February 2024
Line 1:		Line 1:
			{{Orphan\|date=September 2023}}
⚫	~~#Redirect~~ [[Sampling (statistics)]]

			In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

			==Methods==
			The sampling strategies include different techniques.
			# [[Simple random sampling]] is the most common method used for Monte Carlo simulation.<ref>Olken, F., and Rotem, D. (1986). "Simple random sampling from relational databases."</ref> Simple random sampling can be implemented in any programming language and spreadsheet environment with a straightforward {{smallcaps\|rand}} function or its variations.
			# [[Latin hypercube sampling]] is a simple approach that attempts to improve coverage of the multidimensional space compared to simple random sampling but does not always outperform it.<ref>{{cite book \| last1 = Fang \| first1 = K. T. \| last2 = Li
			\| first2 = R. \| last3 = Sudjianto
			\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
			# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
			\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
			\|author2=Dutang, C. \|author3=Savicky, P.
			\|author4=Wuertz, D. \|author5=Knuth, D.
			\|author6=Matsumoto, M. \|author7=Saito, M. \|year=2023 }}</ref> Julia,<ref>{{cite web
			\|title=The QMC module for Julia
			\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
			# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
			\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

			==See also==
		⚫	*[[Sampling (statistics)]]

			==References==
			{{reflist}}

			[[Category:Models of computation]]
			[[Category:Sampling (statistics)]]

MrOllie: Restored revision 1180257744 by MrOllie (talk): This is clearly a duplicate article, will reply on talk

2024-02-12T12:26:30Z

Restored revision 1180257744 by MrOllie (talk): This is clearly a duplicate article, will reply on talk

← Previous revision		Revision as of 12:26, 12 February 2024
Line 1:		Line 1:
		⚫	#Redirect [[Sampling (statistics)]]
	{{Orphan\|date=September 2023}}

	In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

	==Methods==
	The sampling strategies include different techniques.
	# [[Simple random sampling]] is the most common method used for Monte Carlo simulation.<ref>Olken, F., and Rotem, D. (1986). "Simple random sampling from relational databases."</ref> Simple random sampling can be implemented in any programming language and spreadsheet environment with a straightforward {{smallcaps\|rand}} function or its variations.
	# [[Latin hypercube sampling]] is a simple approach that attempts to improve coverage of the multidimensional space compared to simple random sampling but does not always outperform it.<ref>{{cite book \| last1 = Fang \| first1 = K. T. \| last2 = Li
	\| first2 = R. \| last3 = Sudjianto
	\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
	# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
	\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
	\|author2=Dutang, C. \|author3=Savicky, P.
	\|author4=Wuertz, D. \|author5=Knuth, D.
	\|author6=Matsumoto, M. \|author7=Saito, M. \|year=2023 }}</ref> Julia,<ref>{{cite web
	\|title=The QMC module for Julia
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

	==See also==
⚫	*[[Sampling (statistics)]]

	==References==
	{{reflist}}

	[[Category:Models of computation]]
	[[Category:Sampling (statistics)]]

Saltean: Undid revision 1180257744 by MrOllie (talk) Please see the talk page.

2024-02-12T11:36:52Z

Undid revision 1180257744 by MrOllie (talk) Please see the talk page.

← Previous revision		Revision as of 11:36, 12 February 2024
Line 1:		Line 1:
			{{Orphan\|date=September 2023}}
⚫	~~#Redirect~~ [[Sampling (statistics)]]

			In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

			==Methods==
			The sampling strategies include different techniques.
			# [[Simple random sampling]] is the most common method used for Monte Carlo simulation.<ref>Olken, F., and Rotem, D. (1986). "Simple random sampling from relational databases."</ref> Simple random sampling can be implemented in any programming language and spreadsheet environment with a straightforward {{smallcaps\|rand}} function or its variations.
			# [[Latin hypercube sampling]] is a simple approach that attempts to improve coverage of the multidimensional space compared to simple random sampling but does not always outperform it.<ref>{{cite book \| last1 = Fang \| first1 = K. T. \| last2 = Li
			\| first2 = R. \| last3 = Sudjianto
			\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
			# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
			\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
			\|author2=Dutang, C. \|author3=Savicky, P.
			\|author4=Wuertz, D. \|author5=Knuth, D.
			\|author6=Matsumoto, M. \|author7=Saito, M. \|year=2023 }}</ref> Julia,<ref>{{cite web
			\|title=The QMC module for Julia
			\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
			# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
			\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

			==See also==
		⚫	*[[Sampling (statistics)]]

			==References==
			{{reflist}}

			[[Category:Models of computation]]
			[[Category:Sampling (statistics)]]

MrOllie: redirect to existing article on the same topic

2023-10-15T13:52:44Z

redirect to existing article on the same topic

← Previous revision		Revision as of 13:52, 15 October 2023
Line 1:		Line 1:
		⚫	#Redirect [[Sampling (statistics)]]
	{{Orphan\|date=September 2023}}

	In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

	==Methods==
	The sampling strategies include different techniques.
	# [[Simple random sampling]] is the most common method used for Monte Carlo simulation.<ref>Olken, F., and Rotem, D. (1986). "Simple random sampling from relational databases."</ref> Simple random sampling can be implemented in any programming language and spreadsheet environment with a straightforward {{smallcaps\|rand}} function or its variations.
	# [[Latin hypercube sampling]] is a simple approach that attempts to improve coverage of the multidimensional space compared to simple random sampling but does not always outperform it.<ref>{{cite book \| last1 = Fang \| first1 = K. T. \| last2 = Li
	\| first2 = R. \| last3 = Sudjianto
	\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
	# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
	\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
	\|author2=Dutang, C. \|author3=Savicky, P.
	\|author4=Wuertz, D. \|author5=Knuth, D.
	\|author6=Matsumoto, M. \|author7=Saito, M. \|year=2023 }}</ref> Julia,<ref>{{cite web
	\|title=The QMC module for Julia
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

	==See also==
⚫	*[[Sampling (statistics)]]

	==References==
	{{reflist}}

	[[Category:Models of computation]]
	[[Category:Sampling (statistics)]]

Onel5969: clean up, added orphan tag

2023-09-28T10:19:01Z

clean up, added orphan tag

← Previous revision		Revision as of 10:19, 28 September 2023
Line 1:		Line 1:
			{{Orphan\|date=September 2023}}
⚫	In [[Computer simulation\|computational modeling]] and [[computer experiment~~\|computer experiments~~]], '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

		⚫	In [[Computer simulation\|computational modeling]] and [[computer experiment]]s, '''sampling''' is a process of data generation, which is commonly used for [[Monte Carlo method\|Monte Carlo simulation]] and [[uncertainty analysis]], global [[sensitivity analysis]] and other sensitivity and [[design of experiments]] studies, for example with [[heat maps]] and [[Response surface methodology\|response surfaces]].

	==Methods==		==Methods==
Line 14:		Line 16:
	\|title=The QMC module for Julia		\|title=The QMC module for Julia
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>		\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach. <ref>{{cite book		# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach.<ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>		\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

Kozlova Mariia: /* Methods */ grammar fix

2023-09-27T16:57:39Z

Methods: grammar fix

← Previous revision		Revision as of 16:57, 27 September 2023
Line 7:		Line 7:
	\| first2 = R. \| last3 = Sudjianto		\| first2 = R. \| last3 = Sudjianto
	\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>		\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
	# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to ~~feel~~ the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web		# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
	\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.		\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
	\|author2=Dutang, C. \|author3=Savicky, P.		\|author2=Dutang, C. \|author3=Savicky, P.
Line 15:		Line 15:
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>		\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach. <ref>{{cite book		# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach. <ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>		\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

	==See also==		==See also==

← Previous revision		Revision as of 16:57, 27 September 2023
Line 7:		Line 7:
	\| first2 = R. \| last3 = Sudjianto		\| first2 = R. \| last3 = Sudjianto
	\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>		\| first3 = A. \| title = Design and Modeling for Computer Experiments \| year = 2006 \| series = Computer Science and Data Analysis Series}}</ref>
	# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to ~~feel~~ the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web		# [[Quasi-Monte Carlo method\|Quasi-random sampling]] attempts to fill the multidimensional space more uniformly by utilizing [[low-discrepancy sequences]], among which [[Sobol sequence\|Sobol' sequences]] are found the best performing.<ref name="Owen2023">{{cite book \|last=Owen \|first=A. B. \|year=2023 \|title=Practical Quasi-Monte Carlo \|url=https://artowen.su.domains/mc/practicalqmc.pdf }}</ref> Quasi-random sampling functionality is available in Python,<ref>{{cite web\|title=Quasi-monte carlo submodule (scipy.stats.qmc)\|url=https://docs.scipy.org/doc/scipy/reference/stats.qmc.html\|website=SciPy\|year=2023}}</ref> R,<ref>{{cite web
	\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.		\|title=Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests \|url=https://cran.r-project.org/web/packages/randtoolbox/randtoolbox.pdf \|author1=Chalabi, Y.
	\|author2=Dutang, C. \|author3=Savicky, P.		\|author2=Dutang, C. \|author3=Savicky, P.
Line 15:		Line 15:
	\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>		\|url=https://github.com/PieterjanRobbe/QMC.jl \|author=Robbe, P. \|year=2018 }}</ref> and Matlab.<ref>{{cite web \|title=Generating quasi-random numbers \|url=https://se.mathworks.com/help/stats/generating-quasi-random-numbers.html \|author=MathWorks \|year=2013 }}</ref>
	# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach. <ref>{{cite book		# [[Factorial experiment\|Full factorial design]] involves choosing several points for each input variable and then evaluating the model for all their combinations. The computational complexity of this method grows exponentially with the number of input variables involved. Various [[fractional factorial design]] methods have been introduced to improve the computational efficiency of the approach. <ref>{{cite book
	\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>		\|last=Antony \|first=J. \|year=2023 \|title=Design of Experiments for Engineers and Scientists \|publisher=Elsevier }}</ref>

	==See also==		==See also==