Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

CrossRefMATHMathSciNetGoogle ScholarCheng J, Bell DA, Liu W (1997) An algorithm for bayesian belief network construction from data. In: Proceedings of international workshop on artificial intelligence and statistics, pp 83–90

Fearnhead P, Liu Z (2007) Online inference for multiple changepoint problems. J R Stat Soc B 69(4):589–605

CrossRefMathSciNetGoogle ScholarFriedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3):432–441

CrossRefMATHGoogle ScholarGeiger D, Heckerman D (1994) Learning Gaussian networks. Technical report, Microsoft research, mSR-TR-94-10

Grünwald PD (2007) The minimum description length principle. MIT Press, Cambridge

Guo F, Hanneke S, Fu W, Xing EP (2007) Recovering temporally rewiring networks: a model-based approach. In: Proceedings of the 24th international conference on machine learning, pp 321–328

Hayashi Y, Yamanishi K (2012) Sequential network change detection with its applications to ad impact relation analysis. In: Proceedings of the 12th IEEE international conference on data mining, pp 280–289

Hirai S, Yamanishi K (2011) Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering. In: Proceedings of the 2011 IEEE international symposium on information theory, pp 1031–1035

Hirai S, Yamanishi K (2012) Detecting changes of clustering structures using normalized maximum likelihood coding. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 343–351

Hirose S, Yamanishi K, Nakata T, Fujimaki R (2009) Network anomaly detection based on eigen equation compression. In: Proceedings of the 15th ACM SIGKDD conference on knowledge discovery and data mining, pp 1185–1194

Hoeffding W (1963) Probability inequalities for sums of bounded random variables. J Am Stat Assoc 58(301):13–30

CrossRefMATHMathSciNetGoogle ScholarIde T, Kashima H (2004) Eigenspace-based anomaly detection in computer systems. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 440–449

Ide T, Lozano AC, Abe N, Liu Y (2009) Proximity-based anomaly detection using sparse structure learning. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, pp 97–108

Krichevsky RE, Trofimov VK (1981) The performance of universal encoding. IEEE Trans Inf Theory 27(2):199–206

CrossRefMATHMathSciNetGoogle ScholarRissanen J (2000) MDL denoising. IEEE Trans Inf Theory 46(7):2537–2543

CrossRefMATHMathSciNetGoogle ScholarRissanen J (2007) Information and complexity in statistical modeling. Springer, New York

MATHGoogle ScholarSchwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464

CrossRefMATHGoogle ScholarShtarkov YM (1987) Universal sequential coding of single messages. Transl Probl Inf Transm 23(3):3–17

MathSciNetGoogle ScholarSilander T, Myllymäki P (2006) A simple approach for finding the globally optimal Bayesian network structure. In: Proceedings of the 22nd conference on uncertainty in artificial intelligence, pp 445–452

Silander T, Roos T, Kontkanen P, Myllymäki P (2008) Factorized normalized maximum likelihood criterion for learning Bayesian network structures. In: Proceedings of 4th European workshop on probabilistic, graphical models, pp 257–264

Talih M, Hengartner N (2005) Structural learning with time-varying components: tracking the cross-section of financial time series. J R Stat Soc B 67(3):321–341

CrossRefMATHMathSciNetGoogle ScholarViterbi A (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2):260–269

CrossRefMATHGoogle ScholarXuan X, Murphy K (2007) Modeling changing dependency structure in multivariate time series. In: Proceedings of the 24th international conference on machine learning, pp 1055–1062

Yamanishi K, Maruyama Y (2005) Dynamic syslog mining for network failure monitoring. In: Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining, pp 499–508

Yamanishi K, Maruyama Y (2007) Dynamic model selection with its applications to novelty detection. IEEE Trans Inf Theory 53(6):2180–2189

CrossRefMathSciNetGoogle Scholar