Prof. Dr. phil.-hist. Horst Pöttker - Institut für Journalistik

Rottmann M.A. Tobias Schmidt (M.A.) Dipl.-Journ. Tobias Schweigmann Dominik Speck (M.A.) Thomas Terhorst (M.A.) Dr. Astrid Viciano Aleksandra Vujadinovic (M.A.) Dipl.-Journ. Judith Weber Julian Welz (M.A.) Susanne [...] Kristina Beckmann (M.A.) Dipl.-Online-Journ. Tina Bettels-Schwabbauer Dr. Benjamin Bigl Lisa-Marie Eckardt Lukas Erbrich (M.A.) Franziska Gömmel (M.A.) Nico Hornig (M.A.) Lena Marie Hufnagel (M.A.) Niklas E. [...] -Jurist Tobias Kreutzer (M.A.) Leonie Krzistetzko (M.A.) Dr. Timo Lenk Dr. Michel Leroy Nils Makrutzki (M.A.) Dipl.-Jur. Victor Meckenstock Dipl.-Jur. Hanna Püschel Eva-Maria Roehse (M.A.) Dr. Thomas Roessing …

Gaussian Mixture Modeling Introduction – Lecture Notes

References [DeLaRu77] Dempster, A.P., Laird, N.M. and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology) [...] positive semi-definite \(\Sigma\) covariance matrix: rotated ellipsoid (A) \(\Sigma\) diagonal ( variance matrix ): ellipsoid (B) \(\Sigma\) scaled unit matrix: spherical (C) same \(\Sigma\) for all clusters [...] E-step, and the M-step. For Gaussian Mixture modeling, \(\smash {\theta =(\mu_1,\Sigma_1,w_1,\mu_2,\Sigma_2,w_2, \ldots , \mu_k,\Sigma_k,w_k)}\) In contrast to \(k\) -means, we need to use a stopping threshold …

Gaussian Mixture Modeling Introduction – Lecture Notes

References [DeLaRu77] Dempster, A.P., Laird, N.M. and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Statistical Methodology) [...] positive semi-definite \(\Sigma\) covariance matrix: rotated ellipsoid (A) \(\Sigma\) diagonal ( variance matrix ): ellipsoid (B) \(\Sigma\) scaled unit matrix: spherical (C) same \(\Sigma\) for all clusters [...] E-step, and the M-step. For Gaussian Mixture modeling, \(\smash {\theta =(\mu_1,\Sigma_1,w_1,\mu_2,\Sigma_2,w_2, \ldots , \mu_k,\Sigma_k,w_k)}\) In contrast to \(k\) -means, we need to use a stopping threshold …

Microsoft Word - Jatwa_Kar_proof.doc

phosphate buffer (0.15 M, pH 7.4) containing 5 mM EDTA. The homogenate was centrifuged at 2000 X g for 30 min at 4 °C, then the supernatant was incubated with assay buffer, L-T4 (4 μM) and DTT for an hour [...] es and parity in a population study argues against microchi- merism as a trigger of thyroid autoimmu- nity. Eur J Endocrinol 2006;154:39-45. Castilla-Cortazar I, Garcia M, Muguerza B, Quiroga J, Perez [...] Huseini HF, Larijani B, Heshmat R, Fakhrzadeh H, Radjabipour B, Toliat T, Raza M. The efficacy of Silybum marianum (L.) Gaertn. (silymarin) in the treatment of type II diabetes: a randomized, double- blind …

Stochastik (Bayes) Bayes P S∣B= P B∣SP S P B = 0.7⋅0.0014 0.05 ≈0.02 Beispiel S: Schwedin B: Blond P S= 9M 6625M ≈0.0014 P B=0.05 P B∣S =0.7 P A∣B= P B∣AP A P B (grobe Schätzungen, und [...] ormal) S  : Menge der Zustände A : Menge der „Aktionen“ T : Transitionen R : Belohungsfunktion T :S×AS R:S×Aℝ V  s=∑ t=0 ∞  tr t Qopt s,a=R s,aV opt T s ,a Der Wert eines Zustandes für [...] s Beobachtungen Lüdenscheid 0,24     Hidden Markov Models (Formal) P x∣M = ∑ q1,,q l∈Q l ∏ k=l l1 P qk−1qkP qk xk V x∣M = argmax q1 , ,ql∈Q l∏ k=1 l1 P qk−1qkP qk xk Q : Menge der Z …