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Littérature scientifique sur le sujet « Processi di Hawkes »
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Articles de revues sur le sujet "Processi di Hawkes"
Tanuwidjaja, Gunawan, El Sadday A. Oktovianto, Joyie Joyie et Felicia Lisandra S. « DESAIN TAMAN KULINER UNTUK PEDAGANG KAKI LIMA YANG INOVATIF DI TEPI SUNGAI, SURABAYA ». SHARE "SHaring - Action - REflection" 3, no 1 (1 décembre 2015) : 28–34. http://dx.doi.org/10.9744/share.3.1.28-34.
Texte intégralIzun, Tri Alfa, Mesiono Mesiono et Syafri Fadillah Marpaung. « IMPLEMENTASI-BUDAYA-SEKOLAH-ADIWIYATA DI MAN-ASAHAN-KABUPATEN-ASAHAN ». Hijri 10, no 2 (7 décembre 2021) : 12. http://dx.doi.org/10.30821/hijri.v10i2.11259.
Texte intégralFajar Pramono, Muhammad, Syamsulhadi Syamsulhadi, Mudiyono Mudiyono et Sunarru Samsi Hariadi. « PENATAAN DAN PEMBINAAN PEDAGANG KAKI LIMA DALAM PERSPEKTIF KOMUNIKASI PEMBANGUNAN DI SURAKARTA ». KANAL : Jurnal Ilmu Komunikasi 1, no 2 (5 octobre 2016) : 163. http://dx.doi.org/10.21070/kanal.v1i2.336.
Texte intégralWalopka, Yakobus, Marthen L. Ndoen et Sri Suwartiningsih. « FAKTOR-FAKTOR PENYEBAB ANAK JALANAN DI KOTA JAYAPURA, DISTRIK JAYAPURA SELATAN ». KRITIS 27, no 2 (14 septembre 2018) : 92–106. http://dx.doi.org/10.24246/kritis.v27i2p92-106.
Texte intégralTaruna, Zhola Verucha, et Soemaryatmi Soemaryatmi. « Serangan Empat Hari Di Surakarta Sebagai Sumber Penciptaan Karya Tari Tekad ». JCARE:Journal of Choreographic and Artistic Research 1, no 1 (8 novembre 2021) : 25–30. http://dx.doi.org/10.33153/jcare.v1i1.3958.
Texte intégralAricindy, Argitha. « Pelestarian Panganan Tradisional Melalui Pasar Kamu Kawan Lama Dalam Mengembangkan Wisata Kuliner Di Daerah Pantai Labu ». Buddayah : Jurnal Pendidikan Antropologi 4, no 1 (30 juin 2022) : 47. http://dx.doi.org/10.24114/bdh.v4i1.29262.
Texte intégralNeolaka, Melki Jemri Edsion. « PROSES PENCIPTAAN TARI PUJIAN RUMPUT HIJAU : STUDI KASUS TIM INLA INTERNASIONAL BI CAO ». Jurnal KATA 2, no 1 (3 mai 2018) : 37. http://dx.doi.org/10.22216/jk.v2i1.2783.
Texte intégralSari, Galuh Puspita, et Matheus Wasi Bantolo. « KUDA-KUDA PENCAK SILAT DALAM PENCIPTAAN TARI JI KARYA GALUH PUSPITA SARI PADA TAHUN 2018 DI GEDUNG TEATER BESAR ISI SURAKARTA ». Greget : Jurnal Pengetahuan dan Penciptaan Tari 18, no 2 (6 février 2020) : 131–39. http://dx.doi.org/10.33153/grt.v18i2.2869.
Texte intégralKartono, Gamal, Sugito Sugito et Adek Cerah Kurnia Azis. « PENGEMBANGAN BAHAN AJAR BERMUATAN LOKAL BATAK UNTUK SEKOLAH MENENGAH DI KOTA MEDAN ». Gorga : Jurnal Seni Rupa 10, no 1 (25 juin 2021) : 215. http://dx.doi.org/10.24114/gr.v10i1.25971.
Texte intégralSuryandari, Suryandari, Meyninda Destiara et Suwito Singgih. « Pelatihan Laboratorium Virtual Go-Lab dalam Mendukung Merdeka Belajar ». Bubungan Tinggi : Jurnal Pengabdian Masyarakat 4, no 4 (24 décembre 2022) : 1523. http://dx.doi.org/10.20527/btjpm.v4i4.6643.
Texte intégralThèses sur le sujet "Processi di Hawkes"
BRIGNONE, RICCARDO. « Moment based approximations for arithmetic averages with applications in derivative pricing, credit risk and Monte Carlo simulation ». Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262926.
Texte intégralIn this thesis we consider three different financial problems whose solution is related to the arithmetic average of some mean reverting stochastic process, whose distribution is unknown, precluding explicit and exact computations. We propose moment based approximations and examine applications in exotic derivatives pricing, credit risk and Monte Carlo simulation and show that this kind of solution can be very useful as able to reduce the computational cost with respect to alternative numerical methods, which are used as benchmark throughout this work. The first chapter of this thesis is devoted to provide some theoretical background on moment based approximations, including some basic facts on the so-called \textit{moment problem}, common approximations techniques, together with a literature review on the usage of moments in finance and numerical illustrations. In the second chapter, we propose accurate moment based approximation formulas for the price of Asian options in the case where the underlying's price is a mean reverting (with jumps) stochastic process. In the third chapter we introduce an efficient methodology, based on moment matching, for the calibration of the default intensity, which is modeled through an exponential Ornstein-Uhlenbeck process and apply this result to the calculation of Credit Value Adjustment (CVA) in presence of wrong way risk for interest rates derivatives. In the fourth chapter, we consider the problem of simulating stochastic volatility models. Exact simulation schemes have been proposed in literature for various models, but are computationally inefficient due to their dependence on the integral of the variance process, which is generally assumed to be mean reverting and whose distribution is unknown. In this case, we show how to compute the moments of such unknown distribution and develop a new simulation methodology which turns out to be much faster, from a computational point of view, than exact schemes, for a similar level of accuracy. The final chapter is different from the others as moments find only marginal application. We consider a double exponential jump diffusion model where the jump intensity is a stochastic process of Hawkes type. This kind of dynamics has been introduced in literature in order to model jump clustering phenomenon, widely observed in financial and commodity markets. We derive the characteristic function of the integral of log-returns and price geometric Asian options under such model.
CAMPEDELLI, GIAN MARIA. « ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM ». Doctoral thesis, Università Cattolica del Sacro Cuore, 2020. http://hdl.handle.net/10280/70552.
Texte intégralJihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act.