Chapter 2: Introduction to Point Processes
Chapter 2: Introduction to Point Processes I. Point processes are used to describe data that are localized in space or time In Chapter 1, we saw an example of neuronal activity in the supplemental eye field (SEF) expressed in terms of a raster plot and a peri-stimulus time histogram (Fig. 1.1).
Download Chapter 2: Introduction to Point Processes
Information
Domain:
Source:
Link to this page:
Documents from same domain
Lecture 15 Introduction to Survival Analysis
www.stat.columbia.eduLecture 15 Introduction to Survival Analysis BIOST 515 February 26, 2004 BIOST 515, Lecture 15
Two-way ANOVA and ANCOVA
www.stat.columbia.eduTwo-way ANOVA and ANCOVA In this tutorial we discuss fitting two-way analysis of variance (ANOVA), as well as, analysis of covariance (ANCOVA) models in R.
Multiple Linear Regression - Columbia University
www.stat.columbia.eduMultiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the …
Sample size and power calculations - Columbia …
www.stat.columbia.eduCHAPTER 20 Sample size and power calculations 20.1 Choices in the design of data collection Multilevel modeling is typically motivated by features in …
One and Two-sample t-tests - Columbia University
www.stat.columbia.eduOne and Two-sample t-tests The R function t.test() can be used to perform both one and two sample t-tests on vectors of data. The function contains a variety of options and can be called as follows:
Statistics and Research Integrity - Columbia University
www.stat.columbia.eduEuropean Science Editing 13 February 2015; 41(1) Another important challenge was receiving fewer essays from Balkan authors than expected and convincing the referees
Linear Regression - Columbia University
www.stat.columbia.eduLinear Regression In this tutorial we will explore fitting linear regression models using STATA. We will also cover ways of re-expressing variables in a …
The Twentieth-Century Reversal: How Did the …
www.stat.columbia.eduGelman: The Twentieth-Century Reversal: How Did the Republican States Switch to the Democrats and Vice Versa? 3 of, and more …
Abandon Statistical Signi cance - Department of …
www.stat.columbia.eduAbandon Statistical Signi cance Blakeley B. McShane1, David Gal2, Andrew Gelman3, Christian Robert4, and Jennifer L. Tackett1 1Northwestern University, 2University of …
Colors in R - Columbia University
www.stat.columbia.educolor name color name gray8 gray9 gray10 gray11 gray12 gray13 gray14 gray15 gray16 gray17 gray18 gray19 gray20 gray21 gray22 gray23 gray24 gray25 gray26 gray27 gray28
Related documents
Understanding Digital Signal Processing
ptgmedia.pearsoncmg.com1.5 Discrete Linear Systems 12 1.6 Time-Invariant Systems 17 1.7 The Commutative Property of Linear Time-Invariant Systems 18 1.8 Analyzing Linear Time-Invariant Systems 19 References 21 Chapter 1 Problems 23 2 PERIODIC SAMPLING 33 2.1 Aliasing: Signal Ambiguity in the Frequency Domain 33 2.2 Sampling Lowpass Signals 38 2.3 Sampling Bandpass ...
Chapter 4: Discrete-time Fourier Transform (DTFT) 4.1 DTFT ...
abut.sdsu.edu4.1 Chapter 4: Discrete-time Fourier Transform (DTFT) 4.1 DTFT and its Inverse Forward DTFT: The DTFT is a transformation that maps Discrete-time (DT) signal x[n] into a complex valued function of the real variable w, namely: −= ∑ ∈ℜ ∞ =−∞
Convolution - Rutgers University
eceweb1.rutgers.eduthat is, it is applicable to both continuous-and discrete-timelinear systems. In this chapter, we study the convolution concept in the time domain. The slides contain the copyrighted material from Linear Dynamic Systems and Signals, Prentice Hall, …
9.6 Correlation of Discrete-Time Signals - Rutgers University
eceweb1.rutgers.edu9.6 Correlation of Discrete-Time Signals A signal operation similar to signal convolution, but with completely different physical meaning, is signal correlation. The signal correlation operation can be performed either with one signal (autocorrelation) or between two different signals (crosscorrelation).
Course Notes - Purdue University College of Engineering
engineering.purdue.edudigital video. One can obtain discrete-time signals by sampling continuous-time signals (i.e., by selecting only the values of the continuous-time signal at certain intervals). Just as with signals, we can consider continuous-time systems and discrete-time systems. Examples of the former include atmospheric, physical, electrical
Sampling and Reconstruction - Ptolemy Project
ptolemy.berkeley.eduChapter 11 Sampling and Reconstruction Digital hardware, including computers, take actions in discrete steps. So they can deal with discrete-time signals, but they cannot directly handle the continuous-time signals that are prevalent in the physical world. This chapter is about the interface between these two worlds, one continuous, the other ...
CHAPTER Properties of Convolution - Analog Devices
www.analog.comCHAPTER 7 EQUATION 7-1 The delta function is the identity for convolution. Any signal convolved with ... This is the goal of systems that transmit or store signals. b. Amplification & Attenuation ... discrete signals the same as differentiation and integration are used with continuous signals.
Chapter 10 Coherent Optical Communication Systems
www.montana.edu10.2 Multilevel Differential Phase-Shift Keying Multilevel phase-shift keying (M-PSK) is a type of digital modulation format whereby information is encoded into discrete changes D/ k of the phase of the carrier at time instants equal to multiples of the symbol period [26, 29]. Since