Estimation of availability and reliability in CurveBS CurveBS uses the RAFT protocol to maintain consistency of stored data. It generally takes the form of 3 replicas of data. If one replica fails intervention is required to handle the failure according to the actual situation of the system. Estimation of availability and reliability in the three- replicas case Assume that the total number of

Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/23: Cardinality and frequency estimation ??? Vasiliki Kalavri | Boston University 2020 Counting distinct elements 2 ??? Vasiliki Kalavri Boston University 2020 26 • Query approximation error • Error probability Guarantee: The estimation error for frequencies will not exceed with probability • A higher number of hash functions 2003. • Flajolet, Philippe, et al. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. 2007. https://hal.archives-ouvertes.fr/file/index/docid/406166/ filename/FlFuGaMe07

satisfied Feng Li (SDU) Regularization and Bayesian Statistics September 20, 2023 11 / 25 Parameter Estimation in Probabilistic Models Assume data are generated via probabilistic model d ∼ p(d; θ) p(d; θ): Regularization and Bayesian Statistics September 20, 2023 12 / 25 Maximum Likelihood Estimation (MLE) Maximum Likelihood Estimation (MLE): Choose the parameter θ that maximizes the probability of the data, given parameter estimation θMLE = arg max θ ℓ(θ) = arg max θ m � i=1 log p(d(i); θ) Feng Li (SDU) Regularization and Bayesian Statistics September 20, 2023 13 / 25 Maximum-a-Posteriori Estimation (MAP)

................................................................................ 33 11 Cost Estimation .............................................................................................. stories to concisely define the desired system functions and provide the foundation for Agile estimation and planning. They describe what the users want to accomplish with the resulting system. User officer and Agile team?  How is the government monitoring the contractor’s performance? 11 Cost Estimation Estimating costs in an Agile environment requires a more iterative, integrated, and collaborative

[Boehm1981], the effort to build a system with the same size as Debian 3.0 can be estimated. This estimation assumes a “classical”, proprietary development model, 9 0 500000 1e+06 1.5e+06 2e+06 2.5e+06 developed independently from the others, which in nearly all cases is true. For calculating the cost estimation, we have used the mean salary for a full-time systems programmer during 2000, according to Computer and an overhead factor of 2.4 (for an expla- nation on why this factor, and other details of the estimation model, see [Wheeler2001]). 5 Some comments and comparisons The numbers offered in the previous

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s blog # Group example with Parzen-window estimation import numpy from from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = 0 for row in x_samples: x_i = (point_x - row[:, numpy.newaxis]) array([[0], [0]]) # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return result_group('parzen'

call_command', 'clearsessions', schedule_type='H') Groups A group example with Kernel density estimation for probability density functions using the Parzen-window technique. Adapted from Sebastian Raschka’s Group example with Parzen-window estimation import numpy from django_q.tasks import async, result_group, delete_group # the estimation function def parzen_estimation(x_samples, point_x, h): k_n = # async them with a group label to the cache backend for w in widths: async(parzen_estimation, sample, x, w, group='parzen', cached=True) # return after 100 results return