Gaussian Bayesian network comparisons with graph ordering unknown

A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is a...
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Gespeichert in:

Autor*in:	Zhang, Hongmei [verfasserIn] Huang, Xianzheng [verfasserIn] Han, Shengtong [verfasserIn] Rezwan, Faisal I. [verfasserIn] Karmaus, Wilfried [verfasserIn] Arshad, Hasan [verfasserIn] Holloway, John W. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering

Übergeordnetes Werk:	Enthalten in: Computational statistics & data analysis - Amsterdam : Elsevier Science, 1983, 157
Übergeordnetes Werk:	volume:157

DOI / URN:	10.1016/j.csda.2020.107156

Katalog-ID:	ELV005528305

Internformat


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520			\|a A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
650		4	\|a Bayesian methods
650		4	\|a DNA methylation
650		4	\|a Single Queue Equi-Energy
650		4	\|a Differential Gaussian Bayesian network
650		4	\|a Variable selections
650		4	\|a Ordering
700	1		\|a Huang, Xianzheng \|e verfasserin \|4 aut
700	1		\|a Han, Shengtong \|e verfasserin \|4 aut
700	1		\|a Rezwan, Faisal I. \|e verfasserin \|4 aut
700	1		\|a Karmaus, Wilfried \|e verfasserin \|4 aut
700	1		\|a Arshad, Hasan \|e verfasserin \|4 aut
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author_variant	h z hz x h xh s h sh f i r fi fir w k wk h a ha j w h jw jwh
matchkey_str	zhanghongmeihuangxianzhenghanshengtongre:2020----:asinaeinewrcmaiosihrp
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publishDate	2020
allfields	10.1016/j.csda.2020.107156 doi (DE-627)ELV005528305 (ELSEVIER)S0167-9473(20)30247-4 DE-627 ger DE-627 rda eng 004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Zhang, Hongmei verfasserin aut Gaussian Bayesian network comparisons with graph ordering unknown 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons. Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering Huang, Xianzheng verfasserin aut Han, Shengtong verfasserin aut Rezwan, Faisal I. verfasserin aut Karmaus, Wilfried verfasserin aut Arshad, Hasan verfasserin aut Holloway, John W. verfasserin aut Enthalten in Computational statistics & data analysis Amsterdam : Elsevier Science, 1983 157 Online-Ressource (DE-627)27093815X (DE-600)1478763-5 (DE-576)081952511 nnns volume:157 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.73 Mathematische Statistik 31.76 Numerische Mathematik 44.32 Medizinische Mathematik medizinische Statistik AR 157
spelling	10.1016/j.csda.2020.107156 doi (DE-627)ELV005528305 (ELSEVIER)S0167-9473(20)30247-4 DE-627 ger DE-627 rda eng 004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Zhang, Hongmei verfasserin aut Gaussian Bayesian network comparisons with graph ordering unknown 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons. Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering Huang, Xianzheng verfasserin aut Han, Shengtong verfasserin aut Rezwan, Faisal I. verfasserin aut Karmaus, Wilfried verfasserin aut Arshad, Hasan verfasserin aut Holloway, John W. verfasserin aut Enthalten in Computational statistics & data analysis Amsterdam : Elsevier Science, 1983 157 Online-Ressource (DE-627)27093815X (DE-600)1478763-5 (DE-576)081952511 nnns volume:157 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.73 Mathematische Statistik 31.76 Numerische Mathematik 44.32 Medizinische Mathematik medizinische Statistik AR 157
allfields_unstemmed	10.1016/j.csda.2020.107156 doi (DE-627)ELV005528305 (ELSEVIER)S0167-9473(20)30247-4 DE-627 ger DE-627 rda eng 004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Zhang, Hongmei verfasserin aut Gaussian Bayesian network comparisons with graph ordering unknown 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons. Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering Huang, Xianzheng verfasserin aut Han, Shengtong verfasserin aut Rezwan, Faisal I. verfasserin aut Karmaus, Wilfried verfasserin aut Arshad, Hasan verfasserin aut Holloway, John W. verfasserin aut Enthalten in Computational statistics & data analysis Amsterdam : Elsevier Science, 1983 157 Online-Ressource (DE-627)27093815X (DE-600)1478763-5 (DE-576)081952511 nnns volume:157 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.73 Mathematische Statistik 31.76 Numerische Mathematik 44.32 Medizinische Mathematik medizinische Statistik AR 157
allfieldsGer	10.1016/j.csda.2020.107156 doi (DE-627)ELV005528305 (ELSEVIER)S0167-9473(20)30247-4 DE-627 ger DE-627 rda eng 004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Zhang, Hongmei verfasserin aut Gaussian Bayesian network comparisons with graph ordering unknown 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons. Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering Huang, Xianzheng verfasserin aut Han, Shengtong verfasserin aut Rezwan, Faisal I. verfasserin aut Karmaus, Wilfried verfasserin aut Arshad, Hasan verfasserin aut Holloway, John W. verfasserin aut Enthalten in Computational statistics & data analysis Amsterdam : Elsevier Science, 1983 157 Online-Ressource (DE-627)27093815X (DE-600)1478763-5 (DE-576)081952511 nnns volume:157 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.73 Mathematische Statistik 31.76 Numerische Mathematik 44.32 Medizinische Mathematik medizinische Statistik AR 157
allfieldsSound	10.1016/j.csda.2020.107156 doi (DE-627)ELV005528305 (ELSEVIER)S0167-9473(20)30247-4 DE-627 ger DE-627 rda eng 004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Zhang, Hongmei verfasserin aut Gaussian Bayesian network comparisons with graph ordering unknown 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons. Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering Huang, Xianzheng verfasserin aut Han, Shengtong verfasserin aut Rezwan, Faisal I. verfasserin aut Karmaus, Wilfried verfasserin aut Arshad, Hasan verfasserin aut Holloway, John W. verfasserin aut Enthalten in Computational statistics & data analysis Amsterdam : Elsevier Science, 1983 157 Online-Ressource (DE-627)27093815X (DE-600)1478763-5 (DE-576)081952511 nnns volume:157 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.73 Mathematische Statistik 31.76 Numerische Mathematik 44.32 Medizinische Mathematik medizinische Statistik AR 157
language	English
source	Enthalten in Computational statistics & data analysis 157 volume:157
sourceStr	Enthalten in Computational statistics & data analysis 157 volume:157
format_phy_str_mv	Article
bklname	Mathematische Statistik Numerische Mathematik Medizinische Mathematik medizinische Statistik
institution	findex.gbv.de
topic_facet	Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering
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authorswithroles_txt_mv	Zhang, Hongmei @@aut@@ Huang, Xianzheng @@aut@@ Han, Shengtong @@aut@@ Rezwan, Faisal I. @@aut@@ Karmaus, Wilfried @@aut@@ Arshad, Hasan @@aut@@ Holloway, John W. @@aut@@
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author	Zhang, Hongmei
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topic_title	004 DE-600 31.73 bkl 31.76 bkl 44.32 bkl Gaussian Bayesian network comparisons with graph ordering unknown Bayesian methods DNA methylation Single Queue Equi-Energy Differential Gaussian Bayesian network Variable selections Ordering
topic	ddc 004 bkl 31.73 bkl 31.76 bkl 44.32 misc Bayesian methods misc DNA methylation misc Single Queue Equi-Energy misc Differential Gaussian Bayesian network misc Variable selections misc Ordering
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title	Gaussian Bayesian network comparisons with graph ordering unknown
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title_full	Gaussian Bayesian network comparisons with graph ordering unknown
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title_sort	gaussian bayesian network comparisons with graph ordering unknown
title_auth	Gaussian Bayesian network comparisons with graph ordering unknown
abstract	A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
abstractGer	A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
abstract_unstemmed	A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. Findings from theoretical assessment, simulations, and real data applications support the efficacy and efficiency of the proposed method for network comparisons.
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title_short	Gaussian Bayesian network comparisons with graph ordering unknown
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author2	Huang, Xianzheng Han, Shengtong Rezwan, Faisal I. Karmaus, Wilfried Arshad, Hasan Holloway, John W.
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up_date	2024-07-06T18:16:30.401Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV005528305</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524164811.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230504s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.csda.2020.107156</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV005528305</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-9473(20)30247-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.76</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhang, Hongmei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gaussian Bayesian network comparisons with graph ordering unknown</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">A Bayesian approach is proposed that unifies Gaussian Bayesian network constructions and comparisons between two networks (identical or differential) for data with graph ordering unknown. When sampling graph ordering, to escape from local maximums, an adjusted single queue equi-energy algorithm is applied. The conditional posterior probability mass function for network differentiation is derived and its asymptotic proposition is theoretically assessed. Simulations are used to demonstrate the approach and compare with existing methods. Based on epigenetic data at a set of DNA methylation sites (CpG sites), the proposed approach is further examined on its ability to detect network differentiations. 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Gaussian Bayesian network comparisons with graph ordering unknown

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