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	name	time	views
	Session 1.2: Stories from the Field: Methane Leaks	40:23	251
	Session 4.2: Fixing the Carbon Footprint	22:26	275
	Session 2.2: Methane Leak Measurement Hackathon	20:17	139
	Session 2.1: More About Methane Leaks	13:80	247
	L01.1 Lecture Overview	1:52	9,266
	S01.10 Bonferroni's Inequality	9:28	897
	L26.8 Mean First Passage Time	8:44	521
	L13.5 Forecast Revisions	4:38	220
	L21.3 Stochastic Processes	6:21	646
	L19.4 Illustration of the CLT	2:54	194
	L02.1 Lecture Overview	2:70	465
	L25.7 Steady-State Probabilities and Convergence	9:13	165
	L04.9 Multinomial Probabilities	10:36	291
	L04.2 The Counting Principle	11:12	307
	L01.7 A Discrete Example	5:13	858
	L10.10 Detection of a Binary Signal	9:15	207
	L03.5 Conditional Independence	2:46	312
	S18.2 Jensen's Inequality	12:19	2,129
	L17.5 LLMS Example	6:43	128
	L07.1 Lecture Overview	1:50	195
	L09.3 Conditioning Example	3:80	110
	L19.2 The Central Limit Theorem	6:58	273
	S01.7 About the Order of Summation in Series with Multiple Indices	10:50	384
	L05.5 Uniform Random Variables	4:60	226
	L08.6 Exponential Random Variables	8:90	175
	S23.2 Poisson Arrivals During an Exponential Interval	9:37	103
	S01.0 Mathematical Background Overview	1:25	573
	L20.8 Other Natural Estimators	4:37	43
	L16.3 LMS Estimation of One Random Variable Based on Another	9:24	76
	L09.1 Lecture Overview	1:33	126
	L01.10 Interpretations & Uses of Probabilities	3:48	535
	L17.9 The Representation of the Data Matters in LLMS	7:30	51
	L10.3 Comments on Conditional PDFs	4:34	215
	L25.3 Markov Chain Review	6:15	136
	L01.5 Simple Properties of Probabilities	11:50	1,001
	S01.6 The Geometric Series	4:70	340
	L01.2 Sample Space	5:38	2,796
	L26.9 Gambler's Ruin	11:24	164
	L02.2 Conditional Probabilities	9:00	451
	L20.9 Maximum Likelihood Estimation	6:32	73
	L02.5 A Radar Example and Three Basic Tools	10:59	267
	L06.8 Linearity of Expectations & The Mean of the Binomial	8:25	142
	L26.4 A Numerical Example - Part III	10:35	49
	L26.6 Absorption Probabilities	9:58	63
	L22.6 A Simple Example	3:70	49
	L17.7 LLMS with Multiple Observations	6:54	37
	L24.1 Lecture Overview	1:59	113
	L03.2 A Coin Tossing Example	8:00	286
	L25.1 Brief Introduction	1:40	95
	L20.6 Confidence Intervals for the Estimation of the Mean	4:27	96
	L01.9 Countable Additivity	12:10	654
	L10.6 Stick-Breaking Example	10:20	92
	L01.4 Probability Axioms	8:55	1,279
	L02.8 Bayes' Rule	4:28	340
	L24.7 Generic Convergence Questions	5:32	63
	S01.4 When Does a Sequence Converge	2:46	324
	L13.11 Variance of the Sum of a Random Number of Random Variables	5:10	61
	S18.3 Hoeffding's Inequality	18:28	202
	L18.8 Related Topics	6:44	47
	L18.1 Lecture Overview	1:57	63
	L17.6 LLMS for Inferring the Parameter of a Coin	11:29	45
	L10.4 Total Probability & Total Expectation Theorems	5:17	86
	L13.8 A Simple Example	6:29	62
	L14.2 Overview of Some Application Domains	5:17	82
	L23.1 Lecture Overview	1:39	46
	S18.1 Convergence in Probability of the Sum of Two Random Variables	10:13	86
	L09.4 Memorylessness of the Exponential PDF	8:18	92
	L17.4 Remarks on the LLMS Solution and on the Error Variance	8:20	37
	L08.4 Means & Variances	6:57	125
	L21.6 Example: The Distribution of a Busy Period	4:16	51
	L08.7 Cumulative Distribution Functions	12:48	123
	S11.1 Simulation	12:35	85
	L05.11 Linearity of Expectations	3:59	140
	S13.1 Conditional Expectation Properties	8:13	59
	L04.6 A Coin Tossing Example	11:48	182
	L14.4 The Bayesian Inference Framework	9:48	117
	L04.5 Binomial Probabilities	6:38	210
	L21.5 The Fresh Start Property	11:26	55
	L11.3 A Linear Function of a Continuous Random Variable	11:18	83
	L25.4 The Probability of a Path	6:40	62
	L12.2 The Sum of Independent Discrete Random Variables	7:52	82
	L08.2 Probability Density Functions	11:90	147
	L06.4 Conditional PMFs & Expectations Given an Event	7:31	140
	L06.3 The Variance of the Bernoulli & The Uniform	8:40	149
	S01.1 Sets	10:55	475
	L13.3 The Law of Iterated Expectations	3:58	96
	L10.9 Mixed Bayes Rule	8:33	65
	L08.8 Normal Random Variables	9:14	120
	L11.7 The Intuition for the Monotonic Case	5:28	49
	L16.8 Properties of the LMS Estimation Error	5:59	46
	L13.9 Section Means and Variances	9:40	45
	L07.5 Example	4:44	98
	L23.9 Different Sampling Methods can Give Different Results	3:59	29
	S09.1 Buffon's Needle & Monte Carlo Simulation	16:12	124
	L16.4 LMS Performance Evaluation	4:32	41
	L15.5 The Mean Squared Error	13:20	47
	L19.3 Discussion of the CLT	9:00	85
	L12.7 The Variance of the Sum of Random Variables	5:36	86
	L12.5 Covariance	5:54	91
	L08.9 Calculation of Normal Probabilities	10:11	106
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MIT OpenCourseWare 5,896 videos, +2,320,000 subscribers

MIT OpenCourseWare
5,896 videos, +2,320,000 subscribers