Wealth Odyssey is a summary work based on a 12-hour adult education course I taught for 10 years. It’s important to me to educate people through my 29 years in the profession (1994-2023), my focus has always been on helping people first understand that retirement means you’re wealthy enough not to work anymore – working is optional. You don’t need to be rich. Wealth is scalable for any income level and comes from foundation income and investments to supplement that foundation to support your desired lifestyle’s Standard of Individual Living (SOIL) for as long as you live. Your focus should be on your plan and apply a few concepts grounded in well researched evidence.
When people think of financial planning, their first thought is investing. Their second thought is retirement.
Kaplans explain risk succinctly: “Everything is possible, yet only one thing happens.” People understand risk but don’t really understand how to apply it rationally to investing (market risks) or to retirement (longevity risk).
But first, having an understanding of what risk is and isn’t, and where it comes from is important before you can apply it to what fuels your plans – markets and longevity.
This book helped me formulate the basic planning concepts I use in my book since personal finance is all about taking risks – as are any other decisions and actions you take in life.
A compelling journey through history, mathematics, and philosophy, charting humanity’s struggle against randomness
Our lives are played out in the arena of chance. However little we recognize it in our day-to-day existence, we are always riding the odds, seeking out certainty but settling—reluctantly—for likelihood, building our beliefs on the shadowy props of probability. Chances Are is the story of man’s millennia-long search for the tools to manage the recurrent but unpredictable—to help us prevent, or at least mitigate, the seemingly random blows of disaster, disease, and injustice. In these pages, we meet the brilliant individuals who developed the first abstract…
My father, when he consented to talk about all the moments in his life when the odds against his survival were so small as to make them statistically non-existent, would say, ‘I was lucky.’ Trying to understand what he meant got me started on this book. As well as being a novelist, I’m a poker player. Luck is a subject that every poker player has a relationship to; more importantly it’s a subject that every person has a relationship to. The combination of family history and intellectual curiosity and the gambler’s desire to win drove me on this quest.
Sadly, Games, Gods, and Gambling by FN David is out of print.This is the next best thing. Lorraine Daston has the supreme gift of making the complicated idea seem straightforward. This is an account of the frenzy for measuring that happened in the 18th century, and how it made the world we live in today, when the gambler’s eye for odds has become the algorithm of taming chance that guides all our decisions.
What did it mean to be reasonable in the Age of Reason? Classical probabilists from Jakob Bernouli through Pierre Simon Laplace intended their theory as an answer to this question--as "nothing more at bottom than good sense reduced to a calculus," in Laplace's words. In terms that can be easily grasped by nonmathematicians, Lorraine Daston demonstrates how this view profoundly shaped the internal development of probability theory and defined its applications.
We’ve all heard (and likely said), “Correlation is not causation,” but after reading this book you’ll look at both correlation and causation in a whole new way.
Not only is the statistical reasoning in this book flawless, but the examples given are surprising and fascinating (like how a genetics researcher is responsible for the Plinko game on the game show The Price Is Right; he built the pinball-sorting board to model how inherited traits distribute into a bell-shaped curve).
'Wonderful ... illuminating and fun to read' - Daniel Kahneman, winner of the Nobel Prize and author of Thinking, Fast and Slow
'"Pearl's accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and have redefined the term "thinking machine"' - Vint Cerf, Chief Internet Evangelist, Google, Inc.
The influential book in how causality revolutionized science and the world, by the pioneer of artificial intelligence
'Correlation does not imply causation.' This mantra was invoked by scientists for decades in order to avoid taking positions as to whether one thing caused another, such as smoking…
I am Professor of Philosophy of Science at the University of Bristol. I am interested in most areas of contemporary philosophy, in particular the interplay between philosophy and the natural and social sciences. Much of my recent work has focused on evolutionary biology, a science that is replete with implications for traditional philosophical debates about human nature, knowledge, and our place in the world.
This ambitious book, written by a distinguished philosopher, is a contribution to what might be called the “epistemology of evolutionary biology.” Sober starts by offering a general analysis of the concept of evidence based on probability theory, then applies this analysis to issues in the theory of evolution. He explains why the evidence favours evolution over the hypothesis of “intelligent design,” then tackles the thorny methodological problem of how to infer evolutionary history from observations on contemporary species. Though difficult, the book is clearly written and repays close study.
How should the concept of evidence be understood? And how does the concept of evidence apply to the controversy about creationism as well as to work in evolutionary biology about natural selection and common ancestry? In this rich and wide-ranging book, Elliott Sober investigates general questions about probability and evidence and shows how the answers he develops to those questions apply to the specifics of evolutionary biology. Drawing on a set of fascinating examples, he analyzes whether claims about intelligent design are untestable; whether they are discredited by the fact that many adaptations are imperfect; how evidence bears on whether…
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
My knees tremble and my heart quakes when I think of how much work must have gone into these two companion volumes. Collectively, they are more than four times the length of my book, covering the whole of machine learning.
It is an essential encyclopedic resource that should be on the desk of anyone serious about machine learning.
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.
This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.
I’ve wanted to be a philosopher since I read Plato’s Phaedo when I was 17, a new immigrant in Canada. Since then, I’ve been fascinated with time, space, and quantum mechanics and involved in the great debates about their mysteries. I saw probability coming into play more and more in curious roles both in the sciences and in practical life. These five books led me on an exciting journey into the history of probability, the meaning of risk, and the use of probability to assess the possibility of harm. I was gripped, entertained, illuminated, and often amazed at what I was discovering.
I am laughing out loud, even now that I am rereading this book for the umpteenth time. Fraudsters are so clever, and so is advertising. And then there is sloppy journalism with its “wow” statistics.
I like his book enormously, not least because of its witty illustrations. It is subversive, comic, and provocative, and it makes me wise to seductive, misleading practices–and it does so with a light touch.
From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff's lively and engaging primer clarifies the basic principles of statistics and explains how they're used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.
My primary interest is in brain function. Because the principal job of
the brain is to process information, it is necessary to define exactly
what information is. For that, there is no substitute for Claude
Shannon’s theory of information. This theory is not only quite
remarkable in its own right, but it is essential for telecoms,
computers, machine learning (and understanding brain function).
I have written ten "tutorial introduction" books, on topics which vary
from quantum mechanics to AI.
In a parallel universe, I am still an Associate Professor at the
University of Sheffield, England.
This is a more comprehensive and mathematically rigorous book than Pierce’s book. For the novice, it should be read-only after first reading Pierce’s more informal text. Due to its vintage, the layout is fairly cramped, but the content is impeccable. At almost 500 pages, it covers a huge amount of material. This was my main reference book on information theory for many years, but it now sits alongside more recent texts, like MacKay’s book (see below). It is also published by Dover, so it is reasonably priced.
Written for an engineering audience, this book has a threefold purpose: (1) to present elements of modern probability theory — discrete, continuous, and stochastic; (2) to present elements of information theory with emphasis on its basic roots in probability theory; and (3) to present elements of coding theory. The emphasis throughout the book is on such basic concepts as sets, the probability measure associated with sets, sample space, random variables, information measure, and capacity. These concepts proceed from set theory to probability theory and then to information and coding theories. No formal prerequisites are required other than the usual undergraduate…
I am proud to be a human (social) scientist but think that we could collectively achieve a much more successful human science enterprise. And I believe that a better human science would translate into better public policy. Most human scientists focus on their own research, paying little attention to how the broader enterprise functions. I have written many works of a methodological nature over the years. I am pleased to point here to a handful of works with sound advice for enhancing the human science enterprise.
I really liked Williams’ writing style. He is very clear, provides good examples, and is very careful in his argumentation.
I very much liked – and indeed borrowed – his strategy of summarizing the main arguments of each chapter. This is especially important since his book addresses a wide range of challenges in social science. I especially liked his discussion of how the variables we measure are never perfect proxies for the phenomena that we hope to understand.
I also liked his careful discussion of how social scientists need to be more reflective in their work. And I found his discussion of the nature of causation in social science deeply insightful.
Realism and Complexity in Social Science is an argument for a new approach to investigating the social world, that of complex realism. Complex realism brings together a number of strands of thought, in scientific realism, complexity science, probability theory and social research methodology.
It proposes that the reality of the social world is that it is probabilistic, yet there exists enough invariance to make the discovery and explanation of social objects and causal mechanisms possible. This forms the basis for the development of a complex realist foundation for social research, that utilises a number of new and novel approaches to…
I have taught undergraduate and PhD students physics and biophysics for 36 years, and I never get tired of it. I always look for hot new topics and everyday things that we all see but rarely notice as interesting. I also look for “how could anything like that possibly happen at all?”-type questions and the eureka moment when some idea from physics or math pries off the lid, making a seemingly insoluble problem easy. Finally, I look for the skills and frameworks that will open the most doors to students in their future work.
Throughout science, math, finance, and your personal life… underlying almost everything is the concept of a random walk. I love how Mlodinow builds our intuition for this simple yet subtle idea. Yes, the expectation may be zero, but the variance grows without limit—it took me years of study to truly feel that statement and others in my bones, but Mlodinow shows this and much more through vivid, often hilarious examples. It is no exaggeration to say that after Chapter 6, you will understand conditional probability—where so many of us stumble when we think about real situations—better than you would after a whole course on statistics.
And although this is a science book, I challenge anyone to read the last five pages and end up dry-eyed. Years later, as I reread them, the impact is undiminished.
Leonard Mlodinow's The Drunkard's Walk: How Randomness Rules Our Lives is an exhilarating, eye-opening guide to understanding our random world.
Randomness and uncertainty surround everything we do. So why are we so bad at understanding them?
The same tools that help us understand the random paths of molecules can be applied to the randomness that governs so many aspects of our everyday lives, from winning the lottery to road safety, and reveals the truth about the success of sporting heroes and film stars, and even how to make sense of a blood test.
The Drunkard's Walk reveals the psychological illusions…
Most of the one billion people with disabilities in the world are chronically unemployed. Years ago, I set out on a mission to research why that is, and to then attempt to prove that people with disabilities and others are not unemployed for lack of ability. I discovered that we all lack understanding regarding what they need in order to bring their considerable abilities to bare. Fifteen years ago, I founded CY, a for-profit company as a proving ground and showcase for the solutions I found. Over 1,500 employees, 5 weddings, and two court cases later – I have quite a story to tell.
We are primed to measure things against the yardstick of averages, judged according to how closely we come to it or how far we deviate from it. It affects our own self-confidence, how we view and grade our performance and worth.
The assumption that metrics comparing us to an average--like GPAs, personality test results, and performance review ratings--reveal something meaningful about our potential is so ingrained in our consciousness that we don't even question it. That assumption is spectacularly—and scientifically—wrong.
Together with great stories about the folly of averages (the one about pilot chairs really got me) and many research facts and stats it conveys a very important message about how we view the world.
'Must the tyranny of the group rule us from cradle to grave? Absolutely not, says Todd Rose in a subversive and readable introduction to what has been called the new science of the individual ... Readers will be moved' Abigail Zuger, The New York Times
'Groundbreaking ... The man who can teach you how not to be average' Anna Hart, Daily Telegraph
'Fascinating, engaging, and practical. The End of Average will help everyone - and I mean everyone - live up to their potential' Amy Cuddy, author of Presence
'Lively and entertaining ... a cheering story of how the square…
