'Prob' presentation slideshows

Unsupervised learning of natural language morphology

Unsupervised learning of natural language morphology. John Goldsmith University of Chicago May 2005. The goal is to learn about language structure . Controversy: how much structure is innate, hence need not be learned?

1.51k views • 133 slides

Hidden Markov Models

Hidden Markov Models. As used to summarize multiple sequence alignments, and score new sequences. Time flies like an arrow, but fruit flies like a banana. The Markov Property : the state of a system depends only on its previous state.

591 views • 48 slides

Statistics and Quantitative Analysis U4320

624 views • 45 slides

??? ????

??? ????. ?????? ????. ????. 1. ???? 2. ?????? 3. ???????? 4. ?? ???? ???? Approaching Cars. 1. ????. ??????? ?????????? ??? 1 ? ? p ??????? , ? 1- p ??????? ????? ? 1 =(Prob( s 1 = ?? ), Prob( s 1 = ?? ) = ( p , 1- p ) ??? 2 ? ? q ??????? , ? 1- q ???????

793 views • 41 slides

Nir Friedman (opening)

Decision-Principles to Justify Carnap's Updating Method and to Suggest Corrections of Probability Judgments Peter P. Wakker Economics Dept. Maastricht University. Nir Friedman (opening). Good words. Bad words. dimension map density labels player ancestral generative dynamics bound

512 views • 40 slides

Conceptualizing and Understanding Studies of Student Persistence

Conceptualizing and Understanding Studies of Student Persistence. University Planning, Institutional Research, & Accountability April 19, 2007. Overview. Framing the persistence problem Understanding results of retention studies Providing perspective on concepts using IUPUI example.

494 views • 36 slides

Hidden Markov Models

Hidden Markov Models. Modified from: http://www.cs.iastate.edu/~cs544/Lectures/lectures.html. Nucleotide frequencies in the human genome. CpG Islands. Written CpG to distinguish from a C ? G base pair).

426 views • 33 slides

516 views • 27 slides

Hidden Markov Models

Hidden Markov Models. As used to summarize multiple sequence alignments, and score new sequences. Time flies like an arrow, but fruit flies like a banana. The Markov Property : the state of a system depends only on its previous state.

379 views • 25 slides

But Uncertainty is Everywhere

302 views • 23 slides

Forecasting Thunderstorms in Terminal Aerodrome Forecasts (TAFs) Some new insights

Forecasting Thunderstorms in Terminal Aerodrome Forecasts (TAFs) Some new insights. Steven Thompson National Weather Service (NWS) La Crosse, WI. Purpose Identify ways to improve TAF thunderstorm forecasts to:. Reduce false alarm (FA) hours. Increase efficiency of airport operations.

322 views • 21 slides

(1) Schedule Mar 15 Linkage disequilibrium (LD) mapping Mar 17 LD mapping

(1) Schedule Mar 15 Linkage disequilibrium (LD) mapping Mar 17 LD mapping Mar 22 Guest speaker, Dr Yang Mar 24 Overview Attend ENAR Biometrical meeting in Austin from Mar 20 to 23 (2) Projects - Work on a problem learnt in the class Select a problem from your own projects.

379 views • 20 slides

Non-monotonic Reasoning

328 views • 19 slides

CDA6530: Performance Models of Computers and Networks Chapter 1: Review of Practical Probability

CDA6530: Performance Models of Computers and Networks Chapter 1: Review of Practical Probability. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A. Probability Definition.

288 views • 17 slides

Pass 6: Event Classes

Pass 6: Event Classes. The final chapter. Working Definitions TRANSIENT : E > 100 MeV ~ 2 Hz. Maximize Aeff at low energy SOURCE : E> 100 MeV ~ .4 Hz. Maximize Aeff 500 MeV - 10 GeV DIFFUSE : E> 100 MeV ~ .1 Hz. Minimize High energy tail > 10 GeV.

254 views • 17 slides

Process Algebra (2IF45) Probabilistic extension

Process Algebra (2IF45) Probabilistic extension. Dr. Suzana Andova. Probabilistic LTS. Basic ingredients of a PLTS: states non-detereministic states set N probabilistic states set P transitions action transitions labelled with actions and t ? P

262 views • 17 slides

Integrated? Verification using Agda Compiler

Integrated? Verification using Agda Compiler. 2009-09-14 AIM10 Makoto Takeyama AIST/CVS. Basic Idea. Integrated Verification Use Agda to decompose a goal to subgoals. Use automatic tools to check subgoals. Usual approach Modify the type checker to connect to tools. Compiled(?) approach

238 views • 12 slides

Electrostatic potential and energy fall EM lecture, week 2, 7.Oct.2002, Zita, TESC

198 views • 12 slides

PROBABILITY

171 views • 6 slides

Background Files Available

Background Files Available. Processed 500M v7r2 Bkg. events run at SLAC Processed 2M v7r2 All Gammas Processed events through IM scripts Cuts: 1) FilterStatusHi == 0 2) TkrNumTracks > 0 3) CalEnergyRaw > 5 4) CalCsIRLn > 4

142 views • 4 slides