Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Statistics for Spatial Data.

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

SUMMARY
Over the last three years, a major international effort has been made by the Sub-Commission on Earthquake Algorithms of the International Association of Seismology and the Physics of the Earth's Interior (IASPEI) to generate new global traveltime tables for seismic phases to update the tables of Jeffreys & Bullen (1940). The new tables are specifically designed for convenient computational use, with high-accuracy interpolation in both depth and range. The new iasp91 traveltime tables are derived from a radially stratified velocity model which has been constructed so that the times for the major seismic phases are consistent with the reported times for events in the catalogue of the International Seismological Centre (ISC) for the period 1964–1987. The baseline for the P-wave traveltimes in the iasp91 model has been adjusted to provide only a small bias in origin time for well-constrained events at the main nuclear testing sites around the world.

For P-waves at teleseismic distances, the new tables are about 0.7s slower than the 1968 P-tables (Herrin 1968) and on average about 1.8–1.9 s faster than the Jeffreys & Bullen (1940) tables. For S-waves the teleseismic times lie between those of the JB tables and the results of Randall (1971).

Because the times for all phases are derived from the same velocity model, there is complete consistency between the traveltimes for different phases at different focal depths. The calculation scheme adopted for the new iasp91 tables is that proposed by Buland & Chapman (1983). Tables of delay time as a function of slowness are stored for each traveltime branch, and interpolated using a specially designed tau spline which takes care of square-root singularities in the derivative of the traveltime curve at certain critical slownesses. With this representation, once the source depth is specified, it is straightforward to find the traveltime explicitly for a given epicentral distance. The computational cost is no higher than a conventional look-up table, but there is increased accuracy in constructing the traveltimes for a source at arbitrary depth. A further advantage over standard tables is that exactly the same procedure can be used for each phase. For a given source depth, it is therefore possible to generate very rapidly a comprehensive list of traveltimes and associated derivatives for the main seismic phases which could be observed at a given epicentral distance.

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Traveltimes for global earthquake location and phase identification

This article discusses several classes of stochastic models for the origin times and magnitudes of earthquakes. The models are compared for a Japanese data set for the years 1885–1980 using likelihood methods. For the best model, a change of time scale is made to investigate the deviation of the data from the model. Conventional graphical methods associated with stationary Poisson processes can be used with the transformed time scale. For point processes, effective use of such residual analysis makes it possible to find features of the data set that are not captured in the model. Based on such analyses, the utility of seismic quiescence for the prediction of a major earthquake is investigated.

Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes

The Omori formula n(t)=K(t+c)-1 and its modified form n(t)=K(t+c)-P have been successfully applied to many aftershock sequences since the former was proposed just 100 years ago. This paper summarizes studies using these formulae. The problems of fitting these formulae and related point process models to observational data are discussed mainly. Studies published during the last 1/3 century confirmed that the modified Omori formula generally provides an appropriate representation of the temporal variation of aftershock activity. Although no systematic dependence of the index p has been found on the magnitude of the main shock and on the lowest limit of magnitude above which aftershocks are counted, this index (usually p = 0.9-1.5) differs from sequence to. sequence. This variability may be related to the tectonic condition of the region such as structural heterogeneity, stress, and temperature, but it is not clear which factor is most significant in controlling the p value. The constant c is a controversial quantity. It is strongly influenced by incomplete detection of small aftershocks in the early stage of sequence. Careful analyses indicate that c is positive at least for some sequences. Point process models for the temporal pattern of shallow seismicity must include the existence of aftershocks, most suitably expressed by the modified Omori law. Among such models, the ETAS model seems to best represent the main features of seismicity with only five parameters. An anomalous decrease in aftershock activity below the level predicted by the modified Omori formula sometimes precedes a large aftershock. An anomalous decrease in seismic activity of a region below the level predicted by the ETAS model is sometimes followed by a large earthquake in the same or in a neighboring region.

The Centenary of the Omori Formula for a Decay Law of Aftershock Activity

Several space-time statistical models are constructed based on both classical empirical studies of clustering and some more speculative hypotheses. Then we discuss the discrimination between models incorporating contrasting assumptions concerning the form of the space-time clusters. We also examine further practical extensions of the model to situations where the background seismicity is spatially non-homogeneous, and the clusters are non-isotropic. The goodness-of-fit of the models, as measured by AIC values, is discussed for two high quality data sets, in different tectonic regions. AIC also allows the details of the clustering structure in space to be clarified. A simulation algorithm for the models is provided, and used to confirm the numerical accuracy of the likelihood calculations. The simulated data sets show the similar spatial distributions to the real ones, but differ from them in some features of space-time clustering. These differences may provide useful indicators of directions for further study.

Space-Time Point-Process Models for Earthquake Occurrences

A simple and efficient method of simulation is discussed for point processes that are specified by their conditional intensities. The method is based on the thinning algorithm which was introduced recently by Lewis and Shedler for the simulation of nonhomogeneous Poisson processes. Algorithms are given for past dependent point processes containing multivariate processes. The simulations are performed for some parametric conditional intensity functions, and the accuracy of the simulated data is demonstrated by the likelihood ratio test and the minimum Akaike information criterion (AIC) procedure.

/pdf/on-lewis-simulation-method-for-point-processes-11zkz6cliy.pdf

On Lewis' simulation method for point processes

This article is concerned with objective estimation of the spatial intensity function of the background earthquake occurrences from an earthquake catalog that includes numerous clustered events in space and time, and also with an algorithm for producing declustered catalogs from the original catalog. A space-time branching process model (the ETAS model) is used for describing how each event generates offspring events. It is shown that the background intensity function can be evaluated if the total spatial seismicity intensity and the branching structure can be estimated. In fact, the whole space-time process is split into two subprocesses, the background events and the clustered events. The proposed algorithm combines a parametric maximum likelihood estimate for the clustering structures using the space-time ETAS model and a nonparametric estimate of the background seismicity that we call a variable weighted kernel estimate. To demonstrate the present methods, we estimate the background seismic activities...

Yosihiko Ogata

Papers

Statistical Models for Earthquake Occurrences and Residual Analysis for Point Processes

The Centenary of the Omori Formula for a Decay Law of Aftershock Activity

Space-Time Point-Process Models for Earthquake Occurrences

On Lewis' simulation method for point processes

Stochastic declustering of space-time earthquake occurrences