Батарея LSI LSIiBBU08 For MegaRAID SAS 9260/9280 Series (LSI00264 / L5-25343-06)

MegaRAID SAS контроллер LSI 9271-8i

Поддерживаемые уровни RAID 60, 6, 50, 5, 10, 1, 0

Поддержка ОС : Windows 7, Windows Vista, Windows XP x64, Windows XP, Windows Server 2008, Windows Server 2003 x64, Windows Server 2003, Ubuntu, CentOS, Debian Linux, Fedora Core, RedHat Enterprise Linux, SuSE Linux Enterprise Server, FreeBSD, Sun Solaris, Slackware 13.0, Mandriva Free 2010, Scientific Linux 5.5, VMware 4.0/4.1

Чип LSI SAS2208, интегрированный двухъядерный XOR процессор Power PC 800 МГц

Память :

1 Гб DDR-III (DDR3-1333). Возможна установка батареи резервного питания LSIiBBU09 или модуля для сохранения данных из кэша во flash память MegaRAID CacheVault LSICVM01

ПрочееПоддерживается технология LSI CacheCade Pro 2.0. Ключ разблокировки LSI00290 приобретается отдельно

/ L5-25413-18 MegaRAID (PCI-E 3.0, LP, SAS6G, RAID , 8port ,1GB onboard)

/ 05-26106-00 MegaRAID (PCI-E 3.0 x8, LP, SAS 12G, RAID 0,1,10,5, 8port (2*intSFF8643)

03-25420-11A / 05-25420-08 MegaRAID (PCI-E 3.0 x8, LP,SAS 12G, RAID 0,1,10,5,6, 8port (2*intSFF8643),1GB)

MegaRAID (PCIe 3.1 x8 LP, SAS/SATA/NVMe, RAID 0,1,5,10,50, 8port (2*int SFF8643), No cache)

CacheVault Accessory kit for 9361 and 9380 series для контроллеров 2G

CacheVault Accessory kit for 9361 and 9380 series для контроллеров 2G (CVFM06)

PCIe v4 x8 LP, Tri-Mode SAS/SATA/NVMe 12G HBA, 8port(1*int SFF8654), 3808 IOC, RTL {5}

A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes

Mini SAS (SFF-8644 to SFF-8644), 1m for REXP-1220U-RP, REXP-1620U-RP,SAS-12G2E-D,SAS-12G2E-U