Applied Control Poisson Models Online: Fill & Download for Free

This sounds like a really interesting problem actually. Obviously, you can just poll every 5 minutes or something, and for pretty much any realistic application this will work perfectly well.But to actually choose when the best time to poll is, using only information about the number of new articles, is an online control problem.My first idea is to do this as a multi armed bandit, where the arms are intervals of time until the next polling instant, and then you would need to come up with an appropriate function for the reward. You need to be careful with the formulation though. You say that you want to see at least one more article. This should be either exactly one more, or at least one more with some penalty for the amount of time you wait. Else, waiting for an infinite amount of time will be a solution.Let the arms be some set of polling intervals P1 < P2 < … < Pn. You get a reward e*k*exp(-k) each time you poll, where k is the number of new articles on that feed. Apply a bandit algorithm and you have something to start with.Another possibility is to observe a lot of historic posts and use that to fit a poisson process model (with perhaps a time varying rate), then use that to time the polling.

Since you are good at mathematics, I hope that you have some passion on mathematics , while choosing a specialization for higher studies, you have to choose which area will make you to more comfort to move on next steps or further studies. we cant compare pure mathematics and applied mathematics, since each things has it own beauty, there is no perfect way to decide what pure mathematics is and what applied mathematics is. Even mathematicians can’t agree on it! If you’re thinking of studying for a mathematics degree and there are separate courses or departments for pure and applied mathematics at the university you’re applying to, then get in touch and find out exactly what the courses involve.when you are good at solving theorems then the easiest way think about pure mathematics , because most abstract mathematics can have unexpected applications.For example, the branch of mathematics known as “number theory” was once considered one of the most “useless”, but now its plays a vital part in computer encryption systems. If you’ve ever bought something online, you can thank number theorists for letting you do it safely.You could also think about how mathematics relates to other subjects and to the real world. Pure mathematics , is separate from the physical world. It solves problems, finds facts and answers questions that don’t depend on the world around us, but on the rules of mathematics itself.According to my perspective,If you are good at pure mathematics then you can study Master of Advanced Study (MASt).This course, commonly referred to as Part III, is a one-year taught Master's course in mathematics. It is an excellent preparation for mathematical research and it is also a valuable course in mathematics and in its applications for those who want further training before taking posts in industry, teaching, or research establishments.Students admitted from outside Cambridge to Part III study towards the Master of Advanced Study (MASt). Students continuing from the Cambridge Tripos for a fourth year, study towards the Master of Mathematics (MMath).while thinking about Applied mathematics tries to model, predict and explain things in the real world: One example of the power of abstraction is provided by Laplace’s equation, one of the most studied and best understood (non-trivial) partial differential equations in mathematics. A variety of phenomena in astronomy, electromagnetism, and fluid flow are governed by this equation, as is the steady state heat distribution in an object. By understanding the abstract mathematical equation, we simultaneously gain an understanding of all these phenomena.According to my perspective If you are good at applied mathematics and statistics,thenThese are some courses related to statistics;Combinatorics and Graph TheoryThis course is a basic introduction to combinatorics and graph theory for advanced undergraduates in computer science, mathematics, engineering and science. Topics covered include: elements of graph theory; Euler and Hamiltonian circuits; graph coloring; matching; basic counting methods; generating functions; recurrences; inclusion-exclusion; and Polya's theory of counting.Introduction to Numerical Computing with PythonThis course is an introduction to computer programming for numerical computing. The course is based on the computer programming language Python and is suitable for students with no programming or numerical computing background who are interested in taking courses in machine learning, natural language processing, or data science. The course will cover fundamental programming, numerical computing, and numerical linear algebra topics, along with the Python libraries that implement the corresponding data structures and algorithms.Data Visualization and ExplorationIn this course, students will learn the fundamental algorithmic and design principles of visualizing and exploring complex data. The course will cover multiple aspects of data presentation including human perception and design theory; algorithms for exploring patterns in data such as topic modelling, clustering, and dimensionality reduction. A wide range of statistical graphics and information visualization techniques will be covered. We will explore numerical data, relational data, temporal data, spatial data, graphs and text.Introduction to Computer SecurityThis course provides an introduction to the principles and practice of computer and network security with a focus on both fundamentals and practical information. The key topics of this course are applied cryptography; protecting users, data, and services; network security, and common threats and defence strategies.Advanced AlgorithmsPrinciples underlying the design and analysis of efficient algorithms. Topics to be covered include: divide-and-conquer algorithms, graph algorithms, Metroid’s and greedy algorithms, randomized algorithms, NP-completeness, approximation algorithms, linear programming.Probabilistic Graphical ModelsProbabilistic graphical models are an intuitive visual language for describing the structure of joint probability distributions using graphs. They enable the compact representation and manipulation of exponentially large probability distributions, which allows them to efficiently manage the uncertainty and partial observability that commonly occur in real-world problems. As a result, graphical models have become invaluable tools in a wide range of areas from computer vision and sensor networks to natural language processing and computational biology. The aim of this course is to develop the knowledge and skills necessary to effectively design, implement and apply these models to solve real problems.Quantitative Decision MakingSurvey in operations research. Introduction to models and procedures for quantitative analyses of decision problems. Topics include linear programming and extensions, integer programming.Applied Data AnalysisThe basics of data acquisition and analysis, pattern classification, system identification, neural network modeling, and fuzzy systems.Stochastic Processes In Industrial EngineeringIntroduction to the theory of stochastic processes with emphasis on Markov chains, Poisson processes, markovian queues and networks, and computational techniques in Jackson networks. Applications include stochastic models of production systems, reliability and maintenance, and inventory control.Viscous FluidsExact solutions to Navier-Stokes flow and laminar boundary layer flow. Introduction to transition and turbulent boundary layers, and turbulence modeling. Boundary layer stability analysis using pertubation methods.Traffic EngineeringFundamental principles of traffic flow and intersection traffic operations including traffic data collection methods, traffic control devices, traffic signal design, and analysis techniques. Emphasizes quantitative and computerized techniques for designing and optimizing intersection signalization. Several traffic engineering software packages used.Finite Element MethodApplication of numerical methods to solution of problems of structural mechanics. Finite difference techniques and other methods for solution of problems in the vibration, stability, and equilibrium of structural elements.Financial Analysis and DecisionsBasic concepts, principles, and practices involved in financing businesses and in maintaining efficient operation of the firm. Framework for analysing savings-investment and other financial decisions. Both theory and techniques applicable to financial problem solving.Financial ManagementInternal financial problems of firms: capital budgeting, cost of capital, dividend policy, rate of return, and financial aspects of growth. Readings and case-studies.Financial ModelsAnalytical approach to financial management. Emphasis on theoretical topics of financial decision making. Through use of mathematical, statistical, and computer simulation methods, various financial decision making models are made.Theory of Financial MarketsIn-depth study of portfolio analysis and stochastic processes in security markets. Emphasis on quantitative solution techniques and testing procedures.Micro Theory of FinanceOptimum financial policies and decisions of non financial firms. Theory of competition and optimum asset management of financial firms.

Gate 2018 Exam PatternThe GATE Exam is a single paper online examination of three hours, with 65 questions carrying a total of 100 marks.The question paper will consist of both multiple choice questions (MCQ) and numerical answer type questions.In GATE AE, AG, BT, CE, CH, CS, EC, EE, IN, ME, MN, MT, PE, PI, TF and XE papers, the weightage of marks divided into three sections.Engineering Mathematics will carry around 15% of the total marksGeneral Aptitude section will carry 15% of the total marksRemaining 70% percentage of the total marks is devoted to the subject (technical section) of the paper .Syllabus for mechanical with weightage are given below.Section 1: Engineering MathematicsWeightage :- 13–15 marksLinear Algebra: Matrix algebra, systems of linear equations, eigenvalues and eigenvectors.Calculus: Functions of single variable, limit, continuity and differentiability, mean value theorems, indeterminate forms; evaluation of definite and improper integrals; double and triple integrals; partial derivatives, total derivative, Taylor series (in one and two variables), maxima and minima, Fourier series; gradient, divergence and curl, vector identities, directional derivatives, line, surface and volume integrals, applications of Gauss, Stokes and Green’s theorems.Differential equations: First order equations (linear and nonlinear); higher order linear differential equations with constant coefficients; Euler-Cauchy equation; initial and boundary value problems; Laplace transforms; solutions of heat, wave and Laplace's equations.Complex variables: Analytic functions; Cauchy-Riemann equations; Cauchy’s integral theorem and integral formula; Taylor and Laurent series.Probability and Statistics: Definitions of probability, sampling theorems, conditional probability; mean, median, mode and standard deviation; random variables, binomial, Poisson and normal distributions.Numerical Methods: Numerical solutions of linear and non-linear algebraic equations; integration by trapezoidal and Simpson’s rules; single and multi-step methods for differential equations.Section 2: Applied Mechanics and DesignEngineering Mechanics: ( 6–8 marks) Free-body diagrams and equilibrium; trusses and frames; virtual work; kinematics and dynamics of particles and of rigid bodies in plane motion; impulse and momentum (linear and angular) and energy formulations, collisions.Mechanics of Materials: (8–10 mark) Stress and strain, elastic constants, Poisson's ratio; Mohr’s circle for plane stress and plane strain; thin cylinders; shear force and bending moment diagrams; bending and shear stresses; deflection of beams; torsion of circular shafts; Euler’s theory of columns; energy methods; thermal stresses; strain gauges and rosettes; testing of materials with universal testing machine; testing of hardness and impact strength.Theory of Machines: (6–8 marks) Displacement, velocity and acceleration analysis of plane mechanisms; dynamic analysis of linkages; cams; gears and gear trains; flywheels and governors; balancing of reciprocating and rotating masses; gyroscope.Vibrations: (6–8 marks) Free and forced vibration of single degree of freedom systems, effect of damping; vibration isolation; resonance; critical speeds of shafts.Machine Design: (4–6 marks) Design for static and dynamic loading; failure theories; fatigue strength and the S-N diagram; principles of the design of machine elements such as bolted, riveted and welded joints; shafts, gears, rolling and sliding contact bearings, brakes and clutches, springs.Section 3: Fluid Mechanics and Thermal SciencesFluid Mechanics: ( 8–10 marks) Fluid properties; fluid statics, manometry, buoyancy, forces on submerged bodies, stability of floating bodies; control-volume analysis of mass, momentum and energy; fluid acceleration; differential equations of continuity and momentum; Bernoulli’s equation; dimensional analysis; viscous flow of incompressible fluids, boundary layer, elementary turbulent flow, flow through pipes, head losses in pipes, bends and fittings.Heat-Transfer: ( 5–7 marks) Modes of heat transfer; one dimensional heat conduction, resistance concept and electrical analogy, heat transfer through fins; unsteady heat conduction, lumped parameter system, Heisler's charts; thermal boundary layer, dimensionless parameters in free and forced convective heat transfer, heat transfer correlations for flow over flat plates and through pipes, effect of turbulence; heat exchanger performance, LMTD and NTU methods; radiative heat transfer, Stefan-Boltzmann law, Wien's displacement law, black and grey surfaces, view factors, radiation network analysis.Thermodynamics: ( 10–13 mark) Thermodynamic systems and processes; properties of pure substances, behaviour of ideal and real gases; zeroth and first laws of thermodynamics, calculation of work and heat in various processes; second law of thermodynamics; thermodynamic property charts and tables, availability and irreversibility; thermodynamic relations.Applications: Power Engineering: Air and gas compressors; vapour and gas power cycles, concepts of regeneration and reheat. I.C. Engines: Air-standard Otto, Diesel and dual cycles. Refrigeration and air-conditioning: Vapour and gas refrigeration and heat pump cycles; properties of moist air, psychrometric chart, basic psychrometric processes. Turbomachinery: Impulse and reaction principles, velocity diagrams, Pelton-wheel, Francis and Kaplan turbines.Section 4: Materials, Manufacturing and Industrial EngineeringEngineering Materials:( 1–3 mark) Structure and properties of engineering materials, phase diagrams, heat treatment, stress-strain diagrams for engineering materials.Production section covers about 15 marks.Casting, Forming and Joining Processes: Different types of castings, design of patterns, moulds and cores; solidification and cooling; riser and gating design. Plastic deformation and yield criteria; fundamentals of hot and cold working processes; load estimation for bulk (forging, rolling, extrusion, drawing) and sheet (shearing, deep drawing, bending) metal forming processes; principles of powder metallurgy. Principles of welding, brazing, soldering and adhesive bonding.Machining and Machine Tool Operations: Mechanics of machining; basic machine tools; single and multi-point cutting tools, tool geometry and materials, tool life and wear; economics of machining; principles of non-traditional machining processes; principles of work holding, design of jigs and fixtures.Metrology and Inspection: Limits, fits and tolerances; linear and angular measurements; comparators; gauge design; interferometry; form and finish measurement; alignment and testing methods; tolerance analysis in manufacturing and assembly.Computer Integrated Manufacturing: Basic concepts of CAD/CAM and their integration tools.Production Planning and Control: Forecasting models, aggregate production planning, scheduling, materials requirement planning.Inventory Control: Deterministic models; safety stock inventory control systems.Operations Research: Linear programming, simplex method, transportation, assignment, network flow models, simple queuing models, PERT and CPM.Industrial covers 4–7 mark.If this help your upvote and follow me.Thank you