above - Anthropic Principle
... in fact that we have reason to believe that every possible observation is made.9 How can we ever test theories which say that the cosmos is that big? For any observation we specify, such theories assign a very high probability (a probability of one in the case of typical infinite-cosmos theories) to ...
... in fact that we have reason to believe that every possible observation is made.9 How can we ever test theories which say that the cosmos is that big? For any observation we specify, such theories assign a very high probability (a probability of one in the case of typical infinite-cosmos theories) to ...
Fast Iterative Coding Techniques for Feedback Channels
... data acquisition. The stopping functions by which the receiver determines from its observations when to stop data acquisition. By simulating the receiver using the feedback link, the transmitter can also determine when to correspondingly terminate transmission. In particular, at each maps the data o ...
... data acquisition. The stopping functions by which the receiver determines from its observations when to stop data acquisition. By simulating the receiver using the feedback link, the transmitter can also determine when to correspondingly terminate transmission. In particular, at each maps the data o ...
State Executions, Deterrence and the Incidence of
... A panel of U.S. state-level data is employed to examine the relationship between the deterrence probabilities and state per-capita murder rates. The panel covers the years 1978 to 1997 for the 50 states (excluding Washington, D.C.). The beginning and ending dates of the data set were selected for th ...
... A panel of U.S. state-level data is employed to examine the relationship between the deterrence probabilities and state per-capita murder rates. The panel covers the years 1978 to 1997 for the 50 states (excluding Washington, D.C.). The beginning and ending dates of the data set were selected for th ...
Estimation of Parameters and Fitting of Probability
... identically distributed, or i.i.d. An estimate of θ will be a function of X 1 , X 2 , . . . , X n and will hence be a random variable with a probability distribution called its sampling distribution. We will use approximations to the sampling distribution to assess the variability of the estimate, m ...
... identically distributed, or i.i.d. An estimate of θ will be a function of X 1 , X 2 , . . . , X n and will hence be a random variable with a probability distribution called its sampling distribution. We will use approximations to the sampling distribution to assess the variability of the estimate, m ...
1 23 Wisdom of crowds versus groupthink: Conor Mayo-Wilson, Kevin Zollman &
... technologies, but cannot know ahead of time how useful a particular technology will be. Others have suggested applying this model to the choice of treatments by doctors (Berry and Fristedt 1985) crop choices in Africa (Bala and Goyal 2008), choice of drilling sites by oil companies (Keller et al. 20 ...
... technologies, but cannot know ahead of time how useful a particular technology will be. Others have suggested applying this model to the choice of treatments by doctors (Berry and Fristedt 1985) crop choices in Africa (Bala and Goyal 2008), choice of drilling sites by oil companies (Keller et al. 20 ...
Probabilistic Logics and Probabilistic Networks - blogs
... are themselves uncertain. In bioinformatics we are often interested in the probability that a complex molecule ψ is present, given the uncertain presence of molecules ϕ1 , . . . , ϕn . In natural language processing we are interested in the probability that an utterance has semantic structure ψ give ...
... are themselves uncertain. In bioinformatics we are often interested in the probability that a complex molecule ψ is present, given the uncertain presence of molecules ϕ1 , . . . , ϕn . In natural language processing we are interested in the probability that an utterance has semantic structure ψ give ...
Reachability is harder for directed than for undirected finite graphs
... Unfortunately, our nonexpressibility result does not seem to translate into a lower bound on computational complexity. Thus, our results do not give us a proof that directed reachability is harder in some computational complexity sense than undirected reachability. There are two reasons for this, wh ...
... Unfortunately, our nonexpressibility result does not seem to translate into a lower bound on computational complexity. Thus, our results do not give us a proof that directed reachability is harder in some computational complexity sense than undirected reachability. There are two reasons for this, wh ...
Isomorphism and Embedding Problems for Infinite Limits of Scale-Free Graphs.
... Barabási and Albert in [3], motivated in part by the goal of explaining the power-law degree distribution observed in the Internet topology by Faloutsos et al [18] and in the Web topology by Kumar et al [21]. Barabási and Albert’s original paper contained a heuristic argument establishing a power ...
... Barabási and Albert in [3], motivated in part by the goal of explaining the power-law degree distribution observed in the Internet topology by Faloutsos et al [18] and in the Web topology by Kumar et al [21]. Barabási and Albert’s original paper contained a heuristic argument establishing a power ...
Efficient Search for Approximate Nearest Neighbor in High
... using a data structure of size O n(1=) poly log(dn) for Euclidean and other norms. They obtain this result using space partitions induced by spheres, and bucketing. In comparison with our work, they use exponential in d (but not in 1=) storage in contrast with our polynomial in d storage. Their se ...
... using a data structure of size O n(1=) poly log(dn) for Euclidean and other norms. They obtain this result using space partitions induced by spheres, and bucketing. In comparison with our work, they use exponential in d (but not in 1=) storage in contrast with our polynomial in d storage. Their se ...
Ars Conjectandi
Ars Conjectandi (Latin for The Art of Conjecturing) is a book on combinatorics and mathematical probability written by Jakob Bernoulli and published in 1713, eight years after his death, by his nephew, Niklaus Bernoulli. The seminal work consolidated, apart from many combinatorial topics, many central ideas in probability theory, such as the very first version of the law of large numbers: indeed, it is widely regarded as the founding work of that subject. It also addressed problems that today are classified in the twelvefold way, and added to the subjects; consequently, it has been dubbed an important historical landmark in not only probability but all combinatorics by a plethora of mathematical historians. The importance of this early work had a large impact on both contemporary and later mathematicians; for example, Abraham de Moivre.Bernoulli wrote the text between 1684 and 1689, including the work of mathematicians such as Christiaan Huygens, Gerolamo Cardano, Pierre de Fermat, and Blaise Pascal. He incorporated fundamental combinatorial topics such as his theory of permutations and combinations—the aforementioned problems from the twelvefold way—as well as those more distantly connected to the burgeoning subject: the derivation and properties of the eponymous Bernoulli numbers, for instance. Core topics from probability, such as expected value, were also a significant portion of this important work.