623–656‚ July‚ October‚ 1948. A Mathematical Theory of Communication By C. E. SHANNON T I NTRODUCTION HE recent development of various methods of modulation such as PCM and PPM which exchange bandwidth for signal-to-noise ratio has intensified the interest in a general theory of communication. A basis for such a theory is contained in the important papers of Nyquist 1 and Hartley2 on this subject. In the present paper we will extend the theory to include a number of new factors‚ in particular
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“Foundations of Portfolio Theory” by. H.M. Markowitz (1991) Foundations of Portfolio Theory by H.M. Markowitz is based on a two part lesson of microeconomics of capital markets. Part one being that taught by Markowitz‚ which is solely geared toward portfolio theory and how an optimizing investor would behave‚ whereas part two focuses on the Capital Asset Pricing Model (CAPM) which is the work done by Sharpe and Lintner. In this article Markowitz speaks strictly on portfolio theory. He states that there
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Bayesian Theory: An Introduction Bayesian theory is increasingly being adopted by the data scientists and analysts across the world. Most of the times the data set available or the information is incomplete. To deal with this realm of inductive logic‚ usage of probability theory becomes essential. As per the new perceptions‚ probability theory today is recognized as a valid principle of logic that is used for drawing inferences related to hypothesis of interest. E.T. Jaynes in the late 20th century
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bagel. (a) Find the probability distribution for X. (b) Suppose at least 3 people buy a plain bagel. What is the probability that exactly 4 people buy a plain bagel? 3. The probability distribution for a discrete random variable X is given by the formula p(r) = for r = 1‚ 2‚ . . . ‚ 6. (a) Verify that this is a valid probability distribution. (b) Find P (X = 4). (c) Find P (X > 2). (d) Find the probability that X takes on the value 3 or 4. (e) Construct the corresponding probability histogram. 4. Two
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http://www.socialresearchmethods.net/kb/sampaper.php Communication Theory of Secrecy Systems? By C. E. SHANNON 1 INTRODUCTION AND SUMMARY The problems of cryptography and secrecy systems furnish an interesting application of communication theory1. In this paper a theory of secrecy systems is developed. The approach is on a theoretical level and is intended to complement the treatment found in standard works on cryptography2. There‚ a detailed study is made of the many standard types of codes
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ABSTRACT: We study complexity of finding a local minimum in the worst and the average cases. We introduce several neighborhoods and show that the corresponding. In the average case we note that standard local descent algorithm is polynomial. INTRODUCTION: An algorithm is a set of instructions to be followed to solve a problem Worst‚ Average and Best Cases In the previous post‚ we discussed how asymptotic analysis overcomes the problems of naive way of analyzing algorithms
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The Management Of Theory Jungle It was Harold Koontz who introduced the concept of Management Theory Jungle. ’Management Theory Jungle’ was made in an environment where the development of management theory had escalated over a period of two decades. This has resulted to confusion and conflict which many theories have entangled in it. As such‚ it is seen as a jungle. Koontz defined the management theory jungle by identifying and classifying major management theory. Six schools of thoughts were
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Analyzing Waiting Lines Most people find waiting lines irritating – waiting is idle and nonproductive time. From a service system perspective‚ however‚ a line represents a demand for service. Think of a restaurant on a Friday night. As a customer it is an irritation to have to wait 40 plus minutes for a table‚ but from the restaurant’s perspective‚ if there is not a line‚ then that means there are empty tables. Idle services are not good. So management must balance waiting time with the
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Example: large standard deviations let us conclude distribution has lots of variability * Now‚ we will test relationships based on probability Probability – mathematical measure of the likelihood of an even occurring * Chance of the desired even occurring written in %‚ proportion‚ or ration. * 40% chance of rain * Batting average .313 * Probability of a royal flush is
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Modelling Revision Mutually exclusive event- add the probabilities together to find the probability that one or other of the events will occur. E.g men/woman P(A or B)= P(A)+P(B) Non mutually exclusive- shared characteristic P (A or B)= P(A) + P(B) – P(B+A) Independent events – outcome is known to have no effect on another outcome P (A+B) = P(A) X P(B) Dependant events- outcome of one event affects the probability of the outcome of the other. Probability of the second event said to be dependent on the
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